From 4e486fb42166ee68eec1d5ad3fb041d01d16d1db Mon Sep 17 00:00:00 2001 From: borninfreedom Date: Wed, 28 Apr 2021 22:01:44 +0800 Subject: [PATCH 1/8] u --- .gitignore | 3 +- docs/Reinforcement Learning.md | 9 +++++- test/rllib_with_atari.py | 27 ++++++++++++++++++ test/rllib_with_dqn.py | 50 ++++++++++++++++++++++++++++++++++ test/test_atari.py | 9 ++++++ 5 files changed, 96 insertions(+), 2 deletions(-) create mode 100644 test/rllib_with_atari.py create mode 100644 test/rllib_with_dqn.py create mode 100644 test/test_atari.py diff --git a/.gitignore b/.gitignore index d37c11c..d5a2825 100644 --- a/.gitignore +++ b/.gitignore @@ -6,4 +6,5 @@ __pycache__ .vscode .spyproject python_algos -*.out \ No newline at end of file +*.out +algos \ No newline at end of file diff --git a/docs/Reinforcement Learning.md b/docs/Reinforcement Learning.md index d330f5f..cc15c35 100644 --- a/docs/Reinforcement Learning.md +++ b/docs/Reinforcement Learning.md @@ -21,4 +21,11 @@ # Papers * MG Bellemare, Y Naddaf, J Veness, and M Bowling. "The arcade learning environment: An evaluation platform for general agents." Journal of Artificial Intelligence Research (2012). https://github.com/mgbellemare/Arcade-Learning-Environment -* Stella: A Multi-Platform Atari 2600 VCS emulator https://stella-emu.github.io/ \ No newline at end of file +* Stella: A Multi-Platform Atari 2600 VCS emulator https://stella-emu.github.io/ +* [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](http://arxiv.org/abs/1802.01561c) +* PyBullet Quickstart Guide +* [Dueling Network Architectures for Deep Reinforcement Learning](http://arxiv.org/abs/1511.06581) +* [Hindsight Experience Replay](http://arxiv.org/abs/1707.01495) +* [Prioritized Experience Replay](http://arxiv.org/abs/1511.05952) +* [Proximal Policy Optimization Algorithms](http://arxiv.org/abs/1707.06347) +* [Overcoming Exploration in Reinforcement Learning with Demonstrations](http://arxiv.org/abs/1709.10089) \ No newline at end of file diff --git a/test/rllib_with_atari.py b/test/rllib_with_atari.py new file mode 100644 index 0000000..690eab2 --- /dev/null +++ b/test/rllib_with_atari.py @@ -0,0 +1,27 @@ +import ray +from ray import tune +import time +ray.shutdown() +ray.init(ignore_reinit_error=True) + +config = { + 'env': 'Breakout-v0', + 'num_workers':10, + 'num_gpus':1, + 'framework':'torch', + 'render_env':False, + 'num_gpus_per_worker':0, + 'num_envs_per_worker':2, +} +stop = { + 'episode_reward_mean': 500 +} +st=time.time() +results = tune.run( + 'PPO', # Specify the algorithm to train + config=config, + stop=stop +) +print('elapsed time=',time.time()-st) + +ray.shutdown() \ No newline at end of file diff --git a/test/rllib_with_dqn.py b/test/rllib_with_dqn.py new file mode 100644 index 0000000..5ff73c6 --- /dev/null +++ b/test/rllib_with_dqn.py @@ -0,0 +1,50 @@ +import ray +from ray import tune +from ray.rllib.agents.dqn import DQNTrainer + +ray.shutdown() + +# twice the conservative memory settings from https://docs.ray.io/en/master/memory-management.html +ray.init( + ignore_reinit_error=True, +) + +ENV = "BreakoutNoFrameskip-v4" +TARGET_REWARD = 200 +TRAINER = DQNTrainer + +tune.run( + TRAINER, + stop={"episode_reward_mean": TARGET_REWARD}, + config={ + "env": ENV, + "num_gpus": 0, #1, + "monitor": True, + "evaluation_num_episodes": 25, + "num_workers": 0, + # based on https://github.com/ray-project/ray/blob/master/rllib/tuned_examples/dqn/atari-duel-ddqn.yaml + "double_q": True, + "dueling": True, + "num_atoms": 1, + "noisy": False, + "framework":"torch", + "prioritized_replay": False, + "n_step": 1, + "target_network_update_freq": 8000, + "lr": .0000625, + "adam_epsilon": .00015, + "hiddens": [512], + "learning_starts": 20000, + "buffer_size": 400_000, + "rollout_fragment_length": 4, + "train_batch_size": 32, + "exploration_config": { + "epsilon_timesteps": 500_000, + "final_epsilon": 0.01, + }, + "prioritized_replay_alpha": 0.5, + "final_prioritized_replay_beta": 1.0, + "prioritized_replay_beta_annealing_timesteps": 2_000_000, + "timesteps_per_iteration": 10_000, + } +) \ No newline at end of file diff --git a/test/test_atari.py b/test/test_atari.py new file mode 100644 index 0000000..42cfc2b --- /dev/null +++ b/test/test_atari.py @@ -0,0 +1,9 @@ +import gym + +env=gym.make('Breakout-v0') + +print(env) +print(env.observation_space) +print(env.observation_space.sample()) +print(env.action_space) +print(env.action_space.sample()) \ No newline at end of file From db0980a378f936569e8324bc17627500a3ab0e3e Mon Sep 17 00:00:00 2001 From: borninfreedom Date: Wed, 28 Apr 2021 22:25:14 +0800 Subject: [PATCH 2/8] delete ppo of spinup --- ppo/__init__.py | 0 ppo/core.py | 168 --------------- ppo/core_cnn.py | 154 -------------- ppo/core_cnn_cuda.py | 154 -------------- ppo/core_lstm.py | 117 ---------- ppo/logx.py | 386 --------------------------------- ppo/ppo.py | 492 ------------------------------------------- ppo/ppo_cnn.py | 463 ---------------------------------------- ppo/ppo_cnn_cuda.py | 476 ----------------------------------------- ppo/ppo_lstm.py | 464 ---------------------------------------- 10 files changed, 2874 deletions(-) delete mode 100644 ppo/__init__.py delete mode 100644 ppo/core.py delete mode 100644 ppo/core_cnn.py delete mode 100644 ppo/core_cnn_cuda.py delete mode 100644 ppo/core_lstm.py delete mode 100644 ppo/logx.py delete mode 100644 ppo/ppo.py delete mode 100644 ppo/ppo_cnn.py delete mode 100644 ppo/ppo_cnn_cuda.py delete mode 100644 ppo/ppo_lstm.py diff --git a/ppo/__init__.py b/ppo/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/ppo/core.py b/ppo/core.py deleted file mode 100644 index b5d9a7d..0000000 --- a/ppo/core.py +++ /dev/null @@ -1,168 +0,0 @@ -#!/usr/bin/env python3 -# -*- encoding: utf-8 -*- -''' -@File : core.py -@Time : 2021/03/20 14:32:33 -@Author : Yan Wen -@Version : 1.0 -@Contact : z19040042@s.upc.edu.cn -@License : (C)Copyright 2021-2022, Liugroup-NLPR-CASIA -@Desc : None -''' - -# here put the import lib - - -import numpy as np -import scipy.signal -from gym.spaces import Box, Discrete -from ppo.logx import Logger -import torch -import torch.nn as nn -from torch.distributions.normal import Normal -from torch.distributions.categorical import Categorical - - -IS_DEBUG=False -core_logger=Logger(output_dir="../logs/",is_debug=IS_DEBUG) - -def combined_shape(length, shape=None): - if shape is None: - return (length,) - return (length, shape) if np.isscalar(shape) else (length, *shape) - - - -def mlp(sizes, activation, output_activation=nn.Identity): - layers = [] - for j in range(len(sizes) - 1): - act = activation if j < len(sizes) - 2 else output_activation - layers += [nn.Linear(sizes[j], sizes[j + 1]), act()] - core_logger.log("layers={}".format(layers),'green') - core_logger.log("nn.Sequential={}".format(nn.Sequential(*layers))) - - return nn.Sequential(*layers) - - -def count_vars(module): - return sum([np.prod(p.shape) for p in module.parameters()]) - - -def discount_cumsum(x, discount): - """ - magic from rllab for computing discounted cumulative sums of vectors. - - input: - vector x, - [x0, - x1, - x2] - - output: - [x0 + discount * x1 + discount^2 * x2, - x1 + discount * x2, - x2] - """ - return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], axis=0)[::-1] - -class Actor(nn.Module): - - def _distribution(self, obs): - raise NotImplementedError - - def _log_prob_from_distribution(self, pi, act): - raise NotImplementedError - - def forward(self, obs, act=None): - # Produce action distributions for given observations, and - # optionally compute the log likelihood of given actions under - # those distributions. - pi = self._distribution(obs) - logp_a = None - if act is not None: - logp_a = self._log_prob_from_distribution(pi, act) - return pi, logp_a - - -class MLPCategoricalActor(Actor): - - def __init__(self, obs_dim, act_dim, hidden_sizes, activation): - super().__init__() - self.logits_net = mlp([obs_dim] + list(hidden_sizes) + [act_dim], activation) - - def _distribution(self, obs): - logits = self.logits_net(obs) - return Categorical(logits=logits) - - def _log_prob_from_distribution(self, pi, act): - return pi.log_prob(act) - - -class MLPGaussianActor(Actor): - - def __init__(self, obs_dim, act_dim, hidden_sizes, activation): - super().__init__() - log_std = -0.5 * np.ones(act_dim, dtype=np.float32) - self.log_std = torch.nn.Parameter(torch.as_tensor(log_std)) - self.mu_net = mlp([obs_dim] + list(hidden_sizes) + [act_dim], activation) - core_logger.log("mu_net={}".format(self.mu_net)) - - def _distribution(self, obs): - mu = self.mu_net(obs) - std = torch.exp(self.log_std) - core_logger.log("mu={},std={},Normal(mu,std)={}".format(mu,std,Normal(mu,std))) - return Normal(mu, std) - - def _log_prob_from_distribution(self, pi, act): - core_logger.log("pi.log_prob(act)={}".format(pi.log_prob(act)),'red') - core_logger.log("pi.log_prob(act).sum(axis=-1)={}".format(pi.log_prob(act).sum(axis=-1))) - - return pi.log_prob(act).sum(axis=-1) # Last axis sum needed for Torch Normal distribution - - -class MLPCritic(nn.Module): - - def __init__(self, obs_dim, hidden_sizes, activation): - super().__init__() - self.v_net = mlp([obs_dim] + list(hidden_sizes) + [1], activation) - core_logger.log("v_net={}".format(self.v_net)) - - - def forward(self, obs): - core_logger.log("torch.squeeze(self.v_net(obs), -1)".format(torch.squeeze(self.v_net(obs), -1)),'green') - return torch.squeeze(self.v_net(obs), -1) # Critical to ensure v has right shape. - - -class MLPActorCritic(nn.Module): - - def __init__(self, observation_space, action_space, - hidden_sizes=(64, 64), activation=nn.Tanh): - super().__init__() - - obs_dim = observation_space.shape[0] - - # policy builder depends on action space - if isinstance(action_space, Box): - self.pi = MLPGaussianActor(obs_dim, action_space.shape[0], hidden_sizes, activation) - elif isinstance(action_space, Discrete): - self.pi = MLPCategoricalActor(obs_dim, action_space.n, hidden_sizes, activation) - - # build value functionp - self.v = MLPCritic(obs_dim, hidden_sizes, activation) - - def step(self, obs): - with torch.no_grad(): - pi = self.pi._distribution(obs) - core_logger.log("pi={}".format(pi)) - a = pi.sample() - core_logger.log("a={}".format(a)) - logp_a = self.pi._log_prob_from_distribution(pi, a) - core_logger.log("logp_a={}".format(logp_a)) - - v = self.v(obs) - core_logger.log("v={}".format(v),'blue') - return a.numpy(), v.numpy(), logp_a.numpy() - - def act(self, obs): - return self.step(obs)[0] - diff --git a/ppo/core_cnn.py b/ppo/core_cnn.py deleted file mode 100644 index 0da8344..0000000 --- a/ppo/core_cnn.py +++ /dev/null @@ -1,154 +0,0 @@ -import numpy as np -import scipy.signal -from gym.spaces import Box, Discrete - -import torch -import torch.nn as nn -from torch.distributions.normal import Normal -from torch.distributions.categorical import Categorical -import torch.nn.functional as F - - -def combined_shape(length, shape=None): - if shape is None: - return (length, ) - return (length, shape) if np.isscalar(shape) else (length, *shape) - - -class cnn_model(nn.Module): - def __init__(self, num_inputs, num_out, activation=nn.ReLU): - super(cnn_model, self).__init__() - self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1) - self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1) - self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1) - self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1) - - # The output image size is calculated through - # (I - K + 2*P) / S + 1 - # where: I : image size, the initial image size is 84x84, so I == 84 here. - # K : kernel size, here is 3 - # P : padding size, here is 1 - # S : stride, here is 2 - # So 84x84 image will become 6x6 through the ConvNet above. And 32 is the filters number. - self.fc1 = nn.Linear(32 * 6 * 6, 512) - self.fc_out = nn.Linear(512, num_out) - self._initialize_weights() - - def _initialize_weights(self): - for module in self.modules(): - if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear): - nn.init.xavier_uniform_(module.weight) - # nn.init.kaiming_uniform_(module.weight) - nn.init.constant_(module.bias, 0) - elif isinstance(module, nn.LSTMCell): - nn.init.constant_(module.bias_ih, 0) - nn.init.constant_(module.bias_hh, 0) - - def forward(self, x): - x = F.relu(self.conv1(x)) - x = F.relu(self.conv2(x)) - x = F.relu(self.conv3(x)) - x = F.relu(self.conv4(x)) - x = x.view(x.size(0), -1) - x = F.relu(self.fc1(x)) - out = self.fc_out(x) - return out.squeeze() - - -def count_vars(module): - return sum([np.prod(p.shape) for p in module.parameters()]) - - -def discount_cumsum(x, discount): - return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], - axis=0)[::-1] - - -class Actor(nn.Module): - def _distribution(self, obs): - raise NotImplementedError - - def _log_prob_from_distribution(self, pi, act): - raise NotImplementedError - - def forward(self, obs, act=None): - # Produce action distributions for given observations, and - # optionally compute the log likelihood of given actions under - # those distributions. - pi = self._distribution(obs) - logp_a = None - if act is not None: - logp_a = self._log_prob_from_distribution(pi, act) - return pi, logp_a - - -class CNNGaussianActor(Actor): - def __init__(self, obs_dim, act_dim, activation, pretrain=None): - super().__init__() - log_std = -0.5 * np.ones(act_dim, dtype=np.float32) - self.log_std = torch.nn.Parameter(torch.as_tensor(log_std)) - self.mu_net = cnn_model(obs_dim, act_dim, activation=activation) - if pretrain != None: - print('\n\nLoading pretrained from %s.\n\n' % pretrain) - print(self.mu_net) - - def _distribution(self, obs): - mu = self.mu_net(obs) - std = torch.exp(self.log_std) - return Normal(mu, std) - - def _log_prob_from_distribution(self, pi, act): - return pi.log_prob(act).sum(axis=-1) - - # def forward(self, obs, act=None): - # mu = self.mu_net(obs) - # std = torch.exp(self.log_std) - # pi = Normal(mu, std) - # logp_a = None - # if act is not None: - # logp_a = pi.log_prob(act).sum(axis=-1) - # return pi, logp_a - - -class CNNCritic(nn.Module): - def __init__(self, obs_dim, activation): - super().__init__() - # cnn_net([obs_dim] + list(hidden_sizes) + [1], activation) - self.v_net = cnn_model(obs_dim, 1, activation=activation) - print(self.v_net) - - def forward(self, obs): - v = self.v_net(obs) - return torch.squeeze(v, -1) # Critical to ensure v has right shape. - - -class CNNActorCritic(nn.Module): - def __init__(self, - observation_space, - action_space, - hidden_sizes=(64, 64), - activation=nn.Tanh): - super().__init__() - - obs_dim = observation_space.shape[0] - act_dim = action_space.shape[0] - - if isinstance(action_space, Box): - self.pi = CNNGaussianActor(obs_dim, act_dim, activation) - elif isinstance(action_space, Discrete): - #self.pi = MLPCategoricalActor(obs_dim, action_space.n, hidden_sizes, activation) - raise NotImplementedError - - # build value functionp - self.v = CNNCritic(obs_dim, activation) - - def step(self, obs): - with torch.no_grad(): - pi = self.pi._distribution(obs) - a = pi.sample() - logp_a = self.pi._log_prob_from_distribution(pi, a) - v = self.v(obs) - return a.numpy(), v.numpy(), logp_a.numpy() - - def act(self, obs): - return self.step(obs)[0] diff --git a/ppo/core_cnn_cuda.py b/ppo/core_cnn_cuda.py deleted file mode 100644 index 766fa7e..0000000 --- a/ppo/core_cnn_cuda.py +++ /dev/null @@ -1,154 +0,0 @@ -import numpy as np -import scipy.signal -from gym.spaces import Box, Discrete - -import torch -import torch.nn as nn -from torch.distributions.normal import Normal -from torch.distributions.categorical import Categorical -import torch.nn.functional as F - - -def combined_shape(length, shape=None): - if shape is None: - return (length, ) - return (length, shape) if np.isscalar(shape) else (length, *shape) - - -class cnn_model(nn.Module): - def __init__(self, num_inputs, num_out, activation=nn.ReLU): - super(cnn_model, self).__init__() - self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1) - self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1) - self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1) - self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1) - - # The output image size is calculated through - # (I - K + 2*P) / S + 1 - # where: I : image size, the initial image size is 84x84, so I == 84 here. - # K : kernel size, here is 3 - # P : padding size, here is 1 - # S : stride, here is 2 - # So 84x84 image will become 6x6 through the ConvNet above. And 32 is the filters number. - self.fc1 = nn.Linear(32 * 6 * 6, 512) - self.fc_out = nn.Linear(512, num_out) - self._initialize_weights() - - def _initialize_weights(self): - for module in self.modules(): - if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear): - nn.init.xavier_uniform_(module.weight) - # nn.init.kaiming_uniform_(module.weight) - nn.init.constant_(module.bias, 0) - elif isinstance(module, nn.LSTMCell): - nn.init.constant_(module.bias_ih, 0) - nn.init.constant_(module.bias_hh, 0) - - def forward(self, x): - x = F.relu(self.conv1(x)) - x = F.relu(self.conv2(x)) - x = F.relu(self.conv3(x)) - x = F.relu(self.conv4(x)) - x = x.view(x.size(0), -1) - x = F.relu(self.fc1(x)) - out = self.fc_out(x) - return out.squeeze() - - -def count_vars(module): - return sum([np.prod(p.shape) for p in module.parameters()]) - - -def discount_cumsum(x, discount): - return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], - axis=0)[::-1] - - -class Actor(nn.Module): - def _distribution(self, obs): - raise NotImplementedError - - def _log_prob_from_distribution(self, pi, act): - raise NotImplementedError - - def forward(self, obs, act=None): - # Produce action distributions for given observations, and - # optionally compute the log likelihood of given actions under - # those distributions. - pi = self._distribution(obs) - logp_a = None - if act is not None: - logp_a = self._log_prob_from_distribution(pi, act) - return pi, logp_a - - -class CNNGaussianActor(Actor): - def __init__(self, obs_dim, act_dim, activation, pretrain=None): - super().__init__() - log_std = -0.5 * np.ones(act_dim, dtype=np.float32) - self.log_std = torch.nn.Parameter(torch.as_tensor(log_std)) - self.mu_net = cnn_model(obs_dim, act_dim, activation=activation) - if pretrain != None: - print('\n\nLoading pretrained from %s.\n\n' % pretrain) - print(self.mu_net) - - def _distribution(self, obs): - mu = self.mu_net(obs) - std = torch.exp(self.log_std) - return Normal(mu, std) - - def _log_prob_from_distribution(self, pi, act): - return pi.log_prob(act).sum(axis=-1) - - # def forward(self, obs, act=None): - # mu = self.mu_net(obs) - # std = torch.exp(self.log_std) - # pi = Normal(mu, std) - # logp_a = None - # if act is not None: - # logp_a = pi.log_prob(act).sum(axis=-1) - # return pi, logp_a - - -class CNNCritic(nn.Module): - def __init__(self, obs_dim, activation): - super().__init__() - # cnn_net([obs_dim] + list(hidden_sizes) + [1], activation) - self.v_net = cnn_model(obs_dim, 1, activation=activation) - print(self.v_net) - - def forward(self, obs): - v = self.v_net(obs) - return torch.squeeze(v, -1) # Critical to ensure v has right shape. - - -class CNNActorCritic(nn.Module): - def __init__(self, - observation_space, - action_space, - hidden_sizes=(64, 64), - activation=nn.Tanh): - super().__init__() - - obs_dim = observation_space.shape[0] - act_dim = action_space.shape[0] - - if isinstance(action_space, Box): - self.pi = CNNGaussianActor(obs_dim, act_dim, activation) - elif isinstance(action_space, Discrete): - #self.pi = MLPCategoricalActor(obs_dim, action_space.n, hidden_sizes, activation) - raise NotImplementedError - - # build value functionp - self.v = CNNCritic(obs_dim, activation) - - def step(self, obs): - with torch.no_grad(): - pi = self.pi._distribution(obs) - a = pi.sample() - logp_a = self.pi._log_prob_from_distribution(pi, a) - v = self.v(obs) - return a, v, logp_a - - def act(self, obs): - return self.step(obs)[0] diff --git a/ppo/core_lstm.py b/ppo/core_lstm.py deleted file mode 100644 index 59b7a0b..0000000 --- a/ppo/core_lstm.py +++ /dev/null @@ -1,117 +0,0 @@ -import numpy as np -import scipy.signal -from gym.spaces import Box, Discrete - -import torch -import torch.nn as nn -from torch.distributions.normal import Normal -from torch.distributions.categorical import Categorical -import torch.nn.functional as F - -def combined_shape(length, shape=None): - if shape is None: - return (length,) - return (length, shape) if np.isscalar(shape) else (length, *shape) - - -def net(observation_shape, action_shape, activation=nn.ReLU, output_activation=nn.Identity): - model = nn.Sequential(*[ - nn.Linear(np.prod(observation_shape), 128), activation(), - nn.Linear(128, 128), activation(), - nn.Linear(128, 128), activation(), - nn.Linear(128, np.prod(action_shape)) - ]) - return model - -class cnn_model(nn.Module): - def __init__(self, num_inputs, num_out, activation=nn.ReLU): - super(cnn_model, self).__init__() - self.conv1 = nn.Conv2d(num_inputs, 32, 3, stride=2, padding=1) - self.conv2 = nn.Conv2d(32, 32, 3, stride=2, padding=1) - self.conv3 = nn.Conv2d(32, 32, 3, stride=2, padding=1) - self.conv4 = nn.Conv2d(32, 32, 3, stride=2, padding=1) - self.lstm = nn.LSTMCell(32*6*6, 512)#32*6*6, 256)#512, 256) - # self.critic_linear = nn.Linear(512, 1) - # self.actor_linear = nn.Linear(512, num_actions) - self.fc_out = nn.Linear(512, num_out) - self._initialize_weights() - - def _initialize_weights(self): - for module in self.modules(): - if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear): - nn.init.xavier_uniform_(module.weight) - # nn.init.kaiming_uniform_(module.weight) - nn.init.constant_(module.bias, 0) - elif isinstance(module, nn.LSTMCell): - nn.init.constant_(module.bias_ih, 0) - nn.init.constant_(module.bias_hh, 0) - - def forward(self, x, hidden): - #x = x / 255.0 # scale to 0-1 - #print(x.shape) - #x = x.permute(0, 3, 1, 2) # NHWC => NCHW - x = F.relu(self.conv1(x)) - x = F.relu(self.conv2(x)) - x = F.relu(self.conv3(x)) - x = F.relu(self.conv4(x)) - x = x.view(x.size(0), -1) - x ,hidden = self.lstm(x, hidden)#, (torch.zeros((1, 512)), torch.zeros((1, 512))) - out = self.fc_out(x) - return out, (x,hidden) - -def cnn_net(observation_shape, action_shape, activation=nn.ReLU, output_activation=nn.Identity): - model = nn.Sequential(*[ - nn.Conv2d(observation_shape, 32, 3, stride=2, padding=1),activation(), - nn.Conv2d(32, 32, 3, stride=2, padding=1), activation(), - nn.Conv2d(32, 32, 3, stride=2, padding=1), activation(), - nn.Conv2d(32, 32, 3, stride=2, padding=1), activation(), - nn.Linear(32 * 6 * 6, 512), activation(), - nn.Linear(512, np.prod(action_shape)) - ]) - - return model - -def count_vars(module): - return sum([np.prod(p.shape) for p in module.parameters()]) - - -def discount_cumsum(x, discount): - return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], axis=0)[::-1] - - -class userActor(nn.Module): - - def __init__(self, obs_dim, act_dim, hidden_sizes, activation, pretrain=None): - super().__init__() - log_std = -0.5 * np.ones(act_dim, dtype=np.float32) - self.log_std = torch.nn.Parameter(torch.as_tensor(log_std)) - self.mu_net = cnn_model(obs_dim, act_dim, activation=activation) - if pretrain != None: - print('\n\nLoading pretrained from %s.\n\n' % pretrain) - # prams = torch.load(pretrain) - # import copy - # self.logits_net.load_state_dict(prams.state_dict())#copy.deepcopy(prams)) - print(self.mu_net) - - def forward(self, obs, act=None, hidden=None): - mu, hidden=self.mu_net(obs, hidden) - std = torch.exp(self.log_std) - pi = Normal(mu,std) - logp_a = None - if act is not None: - logp_a = pi.log_prob(act).sum(axis=-1) - return pi, logp_a, hidden - - -class userCritic(nn.Module): - - def __init__(self, obs_dim, hidden_sizes, activation): - super().__init__() - self.v_net = cnn_model(obs_dim, 1, activation=activation)#cnn_net([obs_dim] + list(hidden_sizes) + [1], activation) - print(self.v_net) - - def forward(self, obs, hidden): - v, _ = self.v_net(obs, hidden) - return torch.squeeze(v, -1) # Critical to ensure v has right shape. - - diff --git a/ppo/logx.py b/ppo/logx.py deleted file mode 100644 index 0a7b322..0000000 --- a/ppo/logx.py +++ /dev/null @@ -1,386 +0,0 @@ -""" - -Some simple logging functionality, inspired by rllab's logging. - -Logs to a tab-separated-values file (path/to/output_directory/progress.txt) - -""" -import json -import joblib -import shutil -import numpy as np -import tensorflow as tf -import torch -import os.path as osp, time, atexit, os -import warnings -from spinup.utils.mpi_tools import proc_id, mpi_statistics_scalar -from spinup.utils.serialization_utils import convert_json - -color2num = dict( - gray=30, - red=31, - green=32, - yellow=33, - blue=34, - magenta=35, - cyan=36, - white=37, - crimson=38 -) - - -def colorize(string, color, bold=False, highlight=False): - """ - Colorize a string. - - This function was originally written by John Schulman. - """ - attr = [] - num = color2num[color] - if highlight: num += 10 - attr.append(str(num)) - if bold: attr.append('1') - return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string) - - -def restore_tf_graph(sess, fpath): - """ - Loads graphs saved by Logger. - - Will output a dictionary whose keys and values are from the 'inputs' - and 'outputs' dict you specified with logger.setup_tf_saver(). - - Args: - sess: A Tensorflow session. - fpath: Filepath to save directory. - - Returns: - A dictionary mapping from keys to tensors in the computation graph - loaded from ``fpath``. - """ - tf.saved_model.loader.load( - sess, - [tf.saved_model.tag_constants.SERVING], - fpath - ) - model_info = joblib.load(osp.join(fpath, 'model_info.pkl')) - graph = tf.get_default_graph() - model = dict() - model.update({k: graph.get_tensor_by_name(v) for k, v in model_info['inputs'].items()}) - model.update({k: graph.get_tensor_by_name(v) for k, v in model_info['outputs'].items()}) - return model - - -class Logger: - """ - A general-purpose logger. - - Makes it easy to save diagnostics, hyperparameter configurations, the - state of a training run, and the trained model. - """ - - def __init__(self, output_dir=None, output_fname='progress.txt', exp_name=None,is_debug=True): - """ - Initialize a Logger. - - Args: - output_dir (string): A directory for saving results to. If - ``None``, defaults to a temp directory of the form - ``/tmp/experiments/somerandomnumber``. - - output_fname (string): Name for the tab-separated-value file - containing metrics logged throughout a training run. - Defaults to ``progress.txt``. - - exp_name (string): Experiment name. If you run multiple training - runs and give them all the same ``exp_name``, the plotter - will know to group them. (Use case: if you run the same - hyperparameter configuration with multiple random seeds, you - should give them all the same ``exp_name``.) - """ - if proc_id() == 0: - self.output_dir = output_dir or "/tmp/experiments/%i" % int(time.time()) - if osp.exists(self.output_dir): - print("Warning: Log dir %s already exists! Storing info there anyway." % self.output_dir) - else: - os.makedirs(self.output_dir) - self.output_file = open(osp.join(self.output_dir, output_fname), 'w') - atexit.register(self.output_file.close) - print(colorize("Logging data to %s" % self.output_file.name, 'green', bold=True)) - else: - self.output_dir = None - self.output_file = None - self.first_row = True - self.log_headers = [] - self.log_current_row = {} - self.exp_name = exp_name - self.is_debug=is_debug - def log(self, msg, color='green'): - """Print a colorized message to stdout.""" - if self.is_debug: - if proc_id() == 0: - print(colorize(msg, color, bold=True)) - - def log_tabular(self, key, val): - """ - Log a value of some diagnostic. - - Call this only once for each diagnostic quantity, each iteration. - After using ``log_tabular`` to store values for each diagnostic, - make sure to call ``dump_tabular`` to write them out to file and - stdout (otherwise they will not get saved anywhere). - """ - if self.first_row: - self.log_headers.append(key) - else: - assert key in self.log_headers, "Trying to introduce a new key %s that you didn't include in the first iteration" % key - assert key not in self.log_current_row, "You already set %s this iteration. Maybe you forgot to call dump_tabular()" % key - self.log_current_row[key] = val - - def save_config(self, config): - """ - Log an experiment configuration. - - Call this once at the top of your experiment, passing in all important - config vars as a dict. This will serialize the config to JSON, while - handling anything which can't be serialized in a graceful way (writing - as informative a string as possible). - - Example use: - - .. code-block:: python - - logger = EpochLogger(**logger_kwargs) - logger.save_config(locals()) - """ - config_json = convert_json(config) - if self.exp_name is not None: - config_json['exp_name'] = self.exp_name - if proc_id() == 0: - output = json.dumps(config_json, separators=(',', ':\t'), indent=4, sort_keys=True) - print(colorize('Saving config:\n', color='cyan', bold=True)) - print(output) - with open(osp.join(self.output_dir, "config.json"), 'w') as out: - out.write(output) - - def save_state(self, state_dict, itr=None): - """ - Saves the state of an experiment. - - To be clear: this is about saving *state*, not logging diagnostics. - All diagnostic logging is separate from this function. This function - will save whatever is in ``state_dict``---usually just a copy of the - environment---and the most recent parameters for the model you - previously set up saving for with ``setup_tf_saver``. - - Call with any frequency you prefer. If you only want to maintain a - single state and overwrite it at each call with the most recent - version, leave ``itr=None``. If you want to keep all of the states you - save, provide unique (increasing) values for 'itr'. - - Args: - state_dict (dict): Dictionary containing essential elements to - describe the current state of training. - - itr: An int, or None. Current iteration of training. - """ - if proc_id() == 0: - fname = 'vars.pkl' if itr is None else 'vars%d.pkl' % itr - try: - joblib.dump(state_dict, osp.join(self.output_dir, fname)) - except: - self.log('Warning: could not pickle state_dict.', color='red') - if hasattr(self, 'tf_saver_elements'): - self._tf_simple_save(itr) - if hasattr(self, 'pytorch_saver_elements'): - self._pytorch_simple_save(itr) - - def setup_tf_saver(self, sess, inputs, outputs): - """ - Set up easy model saving for tensorflow. - - Call once, after defining your computation graph but before training. - - Args: - sess: The Tensorflow session in which you train your computation - graph. - - inputs (dict): A dictionary that maps from keys of your choice - to the tensorflow placeholders that serve as inputs to the - computation graph. Make sure that *all* of the placeholders - needed for your outputs are included! - - outputs (dict): A dictionary that maps from keys of your choice - to the outputs from your computation graph. - """ - self.tf_saver_elements = dict(session=sess, inputs=inputs, outputs=outputs) - self.tf_saver_info = {'inputs': {k: v.name for k, v in inputs.items()}, - 'outputs': {k: v.name for k, v in outputs.items()}} - - def _tf_simple_save(self, itr=None): - """ - Uses simple_save to save a trained model, plus info to make it easy - to associated tensors to variables after restore. - """ - if proc_id() == 0: - assert hasattr(self, 'tf_saver_elements'), \ - "First have to setup saving with self.setup_tf_saver" - fpath = 'tf1_save' + ('%d' % itr if itr is not None else '') - fpath = osp.join(self.output_dir, fpath) - if osp.exists(fpath): - # simple_save refuses to be useful if fpath already exists, - # so just delete fpath if it's there. - shutil.rmtree(fpath) - tf.saved_model.simple_save(export_dir=fpath, **self.tf_saver_elements) - joblib.dump(self.tf_saver_info, osp.join(fpath, 'model_info.pkl')) - - def setup_pytorch_saver(self, what_to_save): - """ - Set up easy model saving for a single PyTorch model. - - Because PyTorch saving and loading is especially painless, this is - very minimal; we just need references to whatever we would like to - pickle. This is integrated into the logger because the logger - knows where the user would like to save information about this - training run. - - Args: - what_to_save: Any PyTorch model or serializable object containing - PyTorch models. - """ - self.pytorch_saver_elements = what_to_save - - def _pytorch_simple_save(self, itr=None): - """ - Saves the PyTorch model (or models). - """ - if proc_id() == 0: - assert hasattr(self, 'pytorch_saver_elements'), \ - "First have to setup saving with self.setup_pytorch_saver" - fpath = 'pyt_save' - fpath = osp.join(self.output_dir, fpath) - fname = 'model' + ('%d' % itr if itr is not None else '') + '.pt' - fname = osp.join(fpath, fname) - os.makedirs(fpath, exist_ok=True) - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - # We are using a non-recommended way of saving PyTorch models, - # by pickling whole objects (which are dependent on the exact - # directory structure at the time of saving) as opposed to - # just saving network weights. This works sufficiently well - # for the purposes of Spinning Up, but you may want to do - # something different for your personal PyTorch project. - # We use a catch_warnings() context to avoid the warnings about - # not being able to save the source code. - torch.save(self.pytorch_saver_elements, fname) - - def dump_tabular(self): - """ - Write all of the diagnostics from the current iteration. - - Writes both to stdout, and to the output file. - """ - if proc_id() == 0: - vals = [] - key_lens = [len(key) for key in self.log_headers] - max_key_len = max(15, max(key_lens)) - keystr = '%' + '%d' % max_key_len - fmt = "| " + keystr + "s | %15s |" - n_slashes = 22 + max_key_len - print("-" * n_slashes) - for key in self.log_headers: - val = self.log_current_row.get(key, "") - valstr = "%8.3g" % val if hasattr(val, "__float__") else val - print(fmt % (key, valstr)) - vals.append(val) - print("-" * n_slashes, flush=True) - if self.output_file is not None: - if self.first_row: - self.output_file.write("\t".join(self.log_headers) + "\n") - self.output_file.write("\t".join(map(str, vals)) + "\n") - self.output_file.flush() - self.log_current_row.clear() - self.first_row = False - - -class EpochLogger(Logger): - """ - A variant of Logger tailored for tracking average values over epochs. - - Typical use case: there is some quantity which is calculated many times - throughout an epoch, and at the end of the epoch, you would like to - report the average / std / min / max value of that quantity. - - With an EpochLogger, each time the quantity is calculated, you would - use - - .. code-block:: python - - epoch_logger.store(NameOfQuantity=quantity_value) - - to load it into the EpochLogger's state. Then at the end of the epoch, you - would use - - .. code-block:: python - - epoch_logger.log_tabular(NameOfQuantity, **options) - - to record the desired values. - """ - - def __init__(self, *args, **kwargs): - super().__init__(*args, **kwargs) - self.epoch_dict = dict() - - def store(self, **kwargs): - """ - Save something into the epoch_logger's current state. - - Provide an arbitrary number of keyword arguments with numerical - values. - """ - for k, v in kwargs.items(): - if not (k in self.epoch_dict.keys()): - self.epoch_dict[k] = [] - self.epoch_dict[k].append(v) - - def log_tabular(self, key, val=None, with_min_and_max=False, average_only=False): - """ - Log a value or possibly the mean/std/min/max values of a diagnostic. - - Args: - key (string): The name of the diagnostic. If you are logging a - diagnostic whose state has previously been saved with - ``store``, the key here has to match the key you used there. - - val: A value for the diagnostic. If you have previously saved - values for this key via ``store``, do *not* provide a ``val`` - here. - - with_min_and_max (bool): If true, log min and max values of the - diagnostic over the epoch. - - average_only (bool): If true, do not log the standard deviation - of the diagnostic over the epoch. - """ - if val is not None: - super().log_tabular(key, val) - else: - v = self.epoch_dict[key] - vals = np.concatenate(v) if isinstance(v[0], np.ndarray) and len(v[0].shape) > 0 else v - stats = mpi_statistics_scalar(vals, with_min_and_max=with_min_and_max) - super().log_tabular(key if average_only else 'Average' + key, stats[0]) - if not (average_only): - super().log_tabular('Std' + key, stats[1]) - if with_min_and_max: - super().log_tabular('Max' + key, stats[3]) - super().log_tabular('Min' + key, stats[2]) - self.epoch_dict[key] = [] - - def get_stats(self, key): - """ - Lets an algorithm ask the logger for mean/std/min/max of a diagnostic. - """ - v = self.epoch_dict[key] - vals = np.concatenate(v) if isinstance(v[0], np.ndarray) and len(v[0].shape) > 0 else v - return mpi_statistics_scalar(vals) \ No newline at end of file diff --git a/ppo/ppo.py b/ppo/ppo.py deleted file mode 100644 index 5c7918c..0000000 --- a/ppo/ppo.py +++ /dev/null @@ -1,492 +0,0 @@ -#!/usr/bin/env python3 -# -*- encoding: utf-8 -*- -''' -@File : ppo.py -@Time : 2021/03/20 14:32:27 -@Author : Yan Wen -@Version : 1.0 -@Contact : z19040042@s.upc.edu.cn -@License : (C)Copyright 2021-2022, Liugroup-NLPR-CASIA -@Desc : None -''' - -# here put the import lib - -import numpy as np -import torch -from torch.optim import Adam -import gym -import time -import ppo.core as core -from ppo.logx import EpochLogger -from spinup.utils.mpi_pytorch import setup_pytorch_for_mpi, sync_params, mpi_avg_grads -from spinup.utils.mpi_tools import mpi_fork, mpi_avg, proc_id, mpi_statistics_scalar, num_procs -from env.kuka_reach_env import KukaReachEnv -from ppo.logx import Logger -import sys -from colorama import Fore, Back, init - -init(autoreset=True) - -IS_DEBUG = False -ppo_logger = Logger(output_dir="../logs", is_debug=IS_DEBUG) - - -class PPOBuffer: - """ - A buffer for storing trajectories experienced by a PPO agent interacting - with the environment, and using Generalized Advantage Estimation (GAE-Lambda) - for calculating the advantages of state-action pairs. - """ - def __init__(self, obs_dim, act_dim, size, gamma=0.99, lam=0.95): - self.obs_buf = np.zeros(core.combined_shape(size, obs_dim), - dtype=np.float32) - self.act_buf = np.zeros(core.combined_shape(size, act_dim), - dtype=np.float32) - self.adv_buf = np.zeros(size, dtype=np.float32) - self.rew_buf = np.zeros(size, dtype=np.float32) - self.ret_buf = np.zeros(size, dtype=np.float32) - self.val_buf = np.zeros(size, dtype=np.float32) - self.logp_buf = np.zeros(size, dtype=np.float32) - self.gamma, self.lam = gamma, lam - self.ptr, self.path_start_idx, self.max_size = 0, 0, size - - def store(self, obs, act, rew, val, logp): - """ - Append one timestep of agent-environment interaction to the buffer. - """ - assert self.ptr < self.max_size # buffer has to have room so you can store - self.obs_buf[self.ptr] = obs - self.act_buf[self.ptr] = act - self.rew_buf[self.ptr] = rew - self.val_buf[self.ptr] = val - self.logp_buf[self.ptr] = logp - self.ptr += 1 - - def finish_path(self, last_val=0): - """ - Call this at the end of a trajectory, or when one gets cut off - by an epoch ending. This looks back in the buffer to where the - trajectory started, and uses rewards and value estimates from - the whole trajectory to compute advantage estimates with GAE-Lambda, - as well as compute the rewards-to-go for each state, to use as - the targets for the value function. - - The "last_val" argument should be 0 if the trajectory ended - because the agent reached a terminal state (died), and otherwise - should be V(s_T), the value function estimated for the last state. - This allows us to bootstrap the reward-to-go calculation to account - for timesteps beyond the arbitrary episode horizon (or epoch cutoff). - """ - - path_slice = slice(self.path_start_idx, self.ptr) - rews = np.append(self.rew_buf[path_slice], last_val) - vals = np.append(self.val_buf[path_slice], last_val) - - # the next two lines implement GAE-Lambda advantage calculation - deltas = rews[:-1] + self.gamma * vals[1:] - vals[:-1] - self.adv_buf[path_slice] = core.discount_cumsum( - deltas, self.gamma * self.lam) - - # the next line computes rewards-to-go, to be targets for the value function - self.ret_buf[path_slice] = core.discount_cumsum(rews, self.gamma)[:-1] - - self.path_start_idx = self.ptr - - def get(self): - """ - Call this at the end of an epoch to get all of the data from - the buffer, with advantages appropriately normalized (shifted to have - mean zero and std one). Also, resets some pointers in the buffer. - """ - assert self.ptr == self.max_size # buffer has to be full before you can get - self.ptr, self.path_start_idx = 0, 0 - # the next two lines implement the advantage normalization trick - adv_mean, adv_std = mpi_statistics_scalar(self.adv_buf) - self.adv_buf = (self.adv_buf - adv_mean) / adv_std - data = dict(obs=self.obs_buf, - act=self.act_buf, - ret=self.ret_buf, - adv=self.adv_buf, - logp=self.logp_buf) - return { - k: torch.as_tensor(v, dtype=torch.float32) - for k, v in data.items() - } - - -def ppo(env, - actor_critic=core.MLPActorCritic, - ac_kwargs=dict(), - seed=0, - steps_per_epoch=4000, - epochs=50, - gamma=0.99, - clip_ratio=0.2, - pi_lr=3e-4, - vf_lr=1e-3, - train_pi_iters=80, - train_v_iters=80, - lam=0.97, - max_ep_len=1000, - target_kl=0.01, - logger_kwargs=dict(), - save_freq=10): - """ - Proximal Policy Optimization (by clipping), - - with early stopping based on approximate KL - - Args: - env_fn : A function which creates a copy of the environment. - The environment must satisfy the OpenAI Gym API. - - actor_critic: The constructor method for a PyTorch Module with a - ``step`` method, an ``act`` method, a ``pi`` module, and a ``v`` - module. The ``step`` method should accept a batch of observations - and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``a`` (batch, act_dim) | Numpy array of actions for each - | observation. - ``v`` (batch,) | Numpy array of value estimates - | for the provided observations. - ``logp_a`` (batch,) | Numpy array of log probs for the - | actions in ``a``. - =========== ================ ====================================== - - The ``act`` method behaves the same as ``step`` but only returns ``a``. - - The ``pi`` module's forward call should accept a batch of - observations and optionally a batch of actions, and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``pi`` N/A | Torch Distribution object, containing - | a batch of distributions describing - | the policy for the provided observations. - ``logp_a`` (batch,) | Optional (only returned if batch of - | actions is given). Tensor containing - | the log probability, according to - | the policy, of the provided actions. - | If actions not given, will contain - | ``None``. - =========== ================ ====================================== - - The ``v`` module's forward call should accept a batch of observations - and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``v`` (batch,) | Tensor containing the value estimates - | for the provided observations. (Critical: - | make sure to flatten this!) - =========== ================ ====================================== - - - ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object - you provided to PPO. - - seed (int): Seed for random number generators. - - steps_per_epoch (int): Number of steps of interaction (state-action pairs) - for the agent and the environment in each epoch. - - epochs (int): Number of epochs of interaction (equivalent to - number of policy updates) to perform. - - gamma (float): Discount factor. (Always between 0 and 1.) - - clip_ratio (float): Hyperparameter for clipping in the policy objective. - Roughly: how far can the new policy go from the old policy while - still profiting (improving the objective function)? The new policy - can still go farther than the clip_ratio says, but it doesn't help - on the objective anymore. (Usually small, 0.1 to 0.3.) Typically - denoted by :math:`\epsilon`. - - pi_lr (float): Learning rate for policy optimizer. - - vf_lr (float): Learning rate for value function optimizer. - - train_pi_iters (int): Maximum number of gradient descent steps to take - on policy loss per epoch. (Early stopping may cause optimizer - to take fewer than this.) - - train_v_iters (int): Number of gradient descent steps to take on - value function per epoch. - - lam (float): Lambda for GAE-Lambda. (Always between 0 and 1, - close to 1.) - - max_ep_len (int): Maximum length of trajectory / episode / rollout. - - target_kl (float): Roughly what KL divergence we think is appropriate - between new and old policies after an update. This will get used - for early stopping. (Usually small, 0.01 or 0.05.) - - logger_kwargs (dict): Keyword args for EpochLogger. - - save_freq (int): How often (in terms of gap between epochs) to save - the current policy and value function. - - """ - print('called ppo.') - # Special function to avoid certain slowdowns from PyTorch + MPI combo. - setup_pytorch_for_mpi() - - # Set up logger and save configuration - logger = EpochLogger(**logger_kwargs) - logger.save_config(locals()) - - # Random seed - seed += 10000 * proc_id() - torch.manual_seed(seed) - np.random.seed(seed) - - # Instantiate environment - - # modified this to satisfy the custom env - #env = env_fn() - env = env - obs_dim = env.observation_space.shape - act_dim = env.action_space.shape - - # Create actor-critic module - ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs) - ppo_logger.log("ac={}".format(ac), 'green') - - print('ac={}'.format(ac)) - """ - ac=MLPActorCritic( - (pi): MLPGaussianActor( - (mu_net): Sequential( - (0): Linear(in_features=3, out_features=64, bias=True) - (1): Tanh() - (2): Linear(in_features=64, out_features=64, bias=True) - (3): Tanh() - (4): Linear(in_features=64, out_features=3, bias=True) - (5): Identity() - ) - ) - (v): MLPCritic( - (v_net): Sequential( - (0): Linear(in_features=3, out_features=64, bias=True) - (1): Tanh() - (2): Linear(in_features=64, out_features=64, bias=True) - (3): Tanh() - (4): Linear(in_features=64, out_features=1, bias=True) - (5): Identity() - ) - ) -) - """ - # Sync params across processes - sync_params(ac) - - # Count variables - var_counts = tuple(core.count_vars(module) for module in [ac.pi, ac.v]) - logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' % var_counts) - - # Set up experience buffer - local_steps_per_epoch = int(steps_per_epoch / num_procs()) - buf = PPOBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam) - ppo_logger.log("buf={}".format(buf)) - - # Set up function for computing PPO policy loss - def compute_loss_pi(data): - obs, act, adv, logp_old = data['obs'], data['act'], data['adv'], data[ - 'logp'] - ppo_logger.log("obs={}\nact={}\nadv={}\nlogp_old={}".format( - obs, act, adv, logp_old)) - - # Policy loss - pi, logp = ac.pi(obs, act) - ppo_logger.log("pi={},logp={}".format(pi, logp)) - - ratio = torch.exp(logp - logp_old) - clip_adv = torch.clamp(ratio, 1 - clip_ratio, 1 + clip_ratio) * adv - loss_pi = -(torch.min(ratio * adv, clip_adv)).mean() - ppo_logger.log("ratio={},clip_adv={},loss_pi={}".format( - ratio, clip_adv, loss_pi)) - - # Useful extra info - approx_kl = (logp_old - logp).mean().item() - ent = pi.entropy().mean().item() - clipped = ratio.gt(1 + clip_ratio) | ratio.lt(1 - clip_ratio) - clipfrac = torch.as_tensor(clipped, dtype=torch.float32).mean().item() - pi_info = dict(kl=approx_kl, ent=ent, cf=clipfrac) - - return loss_pi, pi_info - - # Set up function for computing value loss - def compute_loss_v(data): - obs, ret = data['obs'], data['ret'] - ppo_logger.log("obs={},ret={},loss_v={}".format( - obs, ret, ((ac.v(obs) - ret)**2).mean())) - return ((ac.v(obs) - ret)**2).mean() - - # Set up optimizers for policy and value function - pi_optimizer = Adam(ac.pi.parameters(), lr=pi_lr) - vf_optimizer = Adam(ac.v.parameters(), lr=vf_lr) - - # Set up model saving - logger.setup_pytorch_saver(ac) - - def update(): - data = buf.get() - - pi_l_old, pi_info_old = compute_loss_pi(data) - #ppo_logger.log("pi_l_old={},pi_info_old={}".format(pi_l_old,pi_info_old)) - - pi_l_old = pi_l_old.item() - v_l_old = compute_loss_v(data).item() - #ppo_logger.log("pi_l_old={},v_l_old={}".format(pi_l_old,v_l_old)) - - # Train policy with multiple steps of gradient descent - for i in range(train_pi_iters): - pi_optimizer.zero_grad() - loss_pi, pi_info = compute_loss_pi(data) - #ppo_logger.log("loss_pi={},pi_info={}".format(loss_pi,pi_info)) - - kl = mpi_avg(pi_info['kl']) - if kl > 1.5 * target_kl: - logger.log( - 'Early stopping at step %d due to reaching max kl.' % i) - break - loss_pi.backward() - mpi_avg_grads(ac.pi) # average grads across MPI processes - pi_optimizer.step() - - logger.store(StopIter=i) - - # Value function learning - for i in range(train_v_iters): - vf_optimizer.zero_grad() - loss_v = compute_loss_v(data) - ppo_logger.log("loss_v={}".format(loss_v)) - loss_v.backward() - mpi_avg_grads(ac.v) # average grads across MPI processes - vf_optimizer.step() - - # Log changes from update - kl, ent, cf = pi_info['kl'], pi_info_old['ent'], pi_info['cf'] - logger.store(LossPi=pi_l_old, - LossV=v_l_old, - KL=kl, - Entropy=ent, - ClipFrac=cf, - DeltaLossPi=(loss_pi.item() - pi_l_old), - DeltaLossV=(loss_v.item() - v_l_old)) - - # Prepare for interaction with environment - start_time = time.time() - o, ep_ret, ep_len = env.reset(), 0, 0 - # ppo_logger.log("o={},ep_ret={},ep_len={}".format(o,ep_ret,ep_len)) - - # Main loop: collect experience in env and update/log each epoch - for epoch in range(epochs): - for t in range(local_steps_per_epoch): - a, v, logp = ac.step(torch.as_tensor(o, dtype=torch.float32)) - # ppo_logger.log("a={},v={},logp={}".format(a,v,logp)) - - # print('a={}'.format(a)) - next_o, r, d, _ = env.step(a) - ep_ret += r - ep_len += 1 - - # save and log - # print(Back.RED+'o={},\na={},\nr={},\nv={},\nlogp={}'.format(o,a,r,v,logp)) - buf.store(o, a, r, v, logp) - logger.store(VVals=v) - - # Update obs (critical!) - o = next_o - - timeout = ep_len == max_ep_len - terminal = d or timeout - epoch_ended = t == local_steps_per_epoch - 1 - - if terminal or epoch_ended: - if epoch_ended and not (terminal): - print('Warning: trajectory cut off by epoch at %d steps.' % - ep_len, - flush=True) - # if trajectory didn't reach terminal state, bootstrap value target - if timeout or epoch_ended: - _, v, _ = ac.step(torch.as_tensor(o, dtype=torch.float32)) - else: - v = 0 - buf.finish_path(v) - if terminal: - # only save EpRet / EpLen if trajectory finished - logger.store(EpRet=ep_ret, EpLen=ep_len) - o, ep_ret, ep_len = env.reset(), 0, 0 - - # Save model - if (epoch % save_freq == 0) or (epoch == epochs - 1): - logger.save_state({'env': env}, None) - - # Perform PPO update! - update() - - # Log info about epoch - logger.log_tabular('Epoch', epoch) - logger.log_tabular('EpRet', with_min_and_max=True) - logger.log_tabular('EpLen', average_only=True) - logger.log_tabular('VVals', with_min_and_max=True) - logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch) - logger.log_tabular('LossPi', average_only=True) - logger.log_tabular('LossV', average_only=True) - logger.log_tabular('DeltaLossPi', average_only=True) - logger.log_tabular('DeltaLossV', average_only=True) - logger.log_tabular('Entropy', average_only=True) - logger.log_tabular('KL', average_only=True) - logger.log_tabular('ClipFrac', average_only=True) - logger.log_tabular('StopIter', average_only=True) - logger.log_tabular('Time', time.time() - start_time) - logger.dump_tabular() - - -if __name__ == '__main__': - - env = KukaReachEnv(is_render=True, is_good_view=True) - - import argparse - - parser = argparse.ArgumentParser() - # parser.add_argument('--env', type=str, default='HalfCheetah-v2') - - #modified this to satisfy the custom env - parser.add_argument('--env', type=str, default=env) - - parser.add_argument('--hid', type=int, default=64) - parser.add_argument('--l', type=int, default=2) - parser.add_argument('--gamma', type=float, default=0.99) - parser.add_argument('--seed', '-s', type=int, default=0) - parser.add_argument('--cpu', type=int, default=4) - parser.add_argument('--steps', type=int, default=4000) - parser.add_argument('--epochs', type=int, default=50) - parser.add_argument('--exp_name', type=str, default='ppo-kuka-reach') - parser.add_argument('--log_dir', type=str, default="../logs") - args = parser.parse_args() - - mpi_fork(args.cpu) # run parallel code with mpi - - from spinup.utils.run_utils import setup_logger_kwargs - - logger_kwargs = setup_logger_kwargs(args.exp_name, - args.seed, - data_dir=args.log_dir) - - ppo(env, - actor_critic=core.MLPActorCritic, - ac_kwargs=dict(hidden_sizes=[args.hid] * args.l), - gamma=args.gamma, - seed=args.seed, - steps_per_epoch=env.max_steps_one_episode * args.cpu, - epochs=100, - logger_kwargs=logger_kwargs) \ No newline at end of file diff --git a/ppo/ppo_cnn.py b/ppo/ppo_cnn.py deleted file mode 100644 index 751c9a1..0000000 --- a/ppo/ppo_cnn.py +++ /dev/null @@ -1,463 +0,0 @@ -import numpy as np -import torch -from torch.optim import Adam -import time -import scipy.signal -import ppo.core_cnn as core -from ppo.logx import EpochLogger -from spinup.utils.mpi_pytorch import setup_pytorch_for_mpi, sync_params, mpi_avg_grads -from spinup.utils.mpi_tools import mpi_fork, mpi_avg, proc_id, mpi_statistics_scalar, num_procs -from colorama import Fore, Back, init -init(autoreset=True) - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - -def combined_shape(length, shape=None): - if shape is None: - return (length, ) - return (length, shape) if np.isscalar(shape) else (length, *shape) - - -def discount_cumsum(x, discount): - """ - magic from rllab for computing discounted cumulative sums of vectors. - - input: - vector x, - [x0, - x1, - x2] - - output: - [x0 + discount * x1 + discount^2 * x2, - x1 + discount * x2, - x2] - """ - return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], - axis=0)[::-1] - - -class PPOBuffer: - """ - A buffer for storing trajectories experienced by a PPO agent interacting - with the environment, and using Generalized Advantage Estimation (GAE-Lambda) - for calculating the advantages of state-action pairs. - """ - def __init__(self, obs_dim, act_dim, size, gamma=0.99, lam=0.95): - self.obs_buf = np.zeros(combined_shape(size, obs_dim), - dtype=np.float32) - self.act_buf = np.zeros(combined_shape(size, act_dim), - dtype=np.float32) - self.adv_buf = np.zeros(size, dtype=np.float32) - self.rew_buf = np.zeros(size, dtype=np.float32) - self.ret_buf = np.zeros(size, dtype=np.float32) - self.val_buf = np.zeros(size, dtype=np.float32) - self.logp_buf = np.zeros(size, dtype=np.float32) - self.gamma, self.lam = gamma, lam - self.ptr, self.path_start_idx, self.max_size = 0, 0, size - - def store(self, obs, act, rew, val, logp): - """ - Append one timestep of agent-environment interaction to the buffer. - """ - assert self.ptr < self.max_size # buffer has to have room so you can store - self.obs_buf[self.ptr] = obs - self.act_buf[self.ptr] = act - self.rew_buf[self.ptr] = rew - self.val_buf[self.ptr] = val - self.logp_buf[self.ptr] = logp - self.ptr += 1 - - def finish_path(self, last_val=0): - """ - Call this at the end of a trajectory, or when one gets cut off - by an epoch ending. This looks back in the buffer to where the - trajectory started, and uses rewards and value estimates from - the whole trajectory to compute advantage estimates with GAE-Lambda, - as well as compute the rewards-to-go for each state, to use as - the targets for the value function. - - The "last_val" argument should be 0 if the trajectory ended - because the agent reached a terminal state (died), and otherwise - should be V(s_T), the value function estimated for the last state. - This allows us to bootstrap the reward-to-go calculation to account - for timesteps beyond the arbitrary episode horizon (or epoch cutoff). - """ - - path_slice = slice(self.path_start_idx, self.ptr) - rews = np.append(self.rew_buf[path_slice], last_val) - vals = np.append(self.val_buf[path_slice], last_val) - - # the next two lines implement GAE-Lambda advantage calculation - deltas = rews[:-1] + self.gamma * vals[1:] - vals[:-1] - self.adv_buf[path_slice] = discount_cumsum(deltas, - self.gamma * self.lam) - - # the next line computes rewards-to-go, to be targets for the value function - self.ret_buf[path_slice] = discount_cumsum(rews, self.gamma)[:-1] - - self.path_start_idx = self.ptr - - def get(self): - """ - Call this at the end of an epoch to get all of the data from - the buffer, with advantages appropriately normalized (shifted to have - mean zero and std one). Also, resets some pointers in the buffer. - """ - assert self.ptr == self.max_size # buffer has to be full before you can get - self.ptr, self.path_start_idx = 0, 0 - # the next two lines implement the advantage normalization trick - adv_mean, adv_std = mpi_statistics_scalar(self.adv_buf) - self.adv_buf = (self.adv_buf - adv_mean) / adv_std - data = dict(obs=self.obs_buf, - act=self.act_buf, - ret=self.ret_buf, - adv=self.adv_buf, - logp=self.logp_buf) - #data.to(device) - return { - k: torch.as_tensor(v, dtype=torch.float32) - for k, v in data.items() - } - - -def ppo(env_fn, - actor_critic=core.CNNActorCritic, - ac_kwargs=dict(), - seed=0, - steps_per_epoch=4000, - epochs=50, - gamma=0.99, - clip_ratio=0.2, - pi_lr=3e-4, - vf_lr=1e-3, - train_pi_iters=80, - train_v_iters=80, - lam=0.97, - max_ep_len=1000, - target_kl=0.01, - logger_kwargs=dict(), - save_freq=10, - pretrain=None): - """ - Proximal Policy Optimization (by clipping), - - with early stopping based on approximate KL - - Args: - env_fn : A function which creates a copy of the environment. - The environment must satisfy the OpenAI Gym API. - - actor_critic: The constructor method for a PyTorch Module with a - ``step`` method, an ``act`` method, a ``pi`` module, and a ``v`` - module. The ``step`` method should accept a batch of observations - and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``a`` (batch, act_dim) | Numpy array of actions for each - | observation. - ``v`` (batch,) | Numpy array of value estimates - | for the provided observations. - ``logp_a`` (batch,) | Numpy array of log probs for the - | actions in ``a``. - =========== ================ ====================================== - - The ``act`` method behaves the same as ``step`` but only returns ``a``. - - The ``pi`` module's forward call should accept a batch of - observations and optionally a batch of actions, and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``pi`` N/A | Torch Distribution object, containing - | a batch of distributions describing - | the policy for the provided observations. - ``logp_a`` (batch,) | Optional (only returned if batch of - | actions is given). Tensor containing - | the log probability, according to - | the policy, of the provided actions. - | If actions not given, will contain - | ``None``. - =========== ================ ====================================== - - The ``v`` module's forward call should accept a batch of observations - and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``v`` (batch,) | Tensor containing the value estimates - | for the provided observations. (Critical: - | make sure to flatten this!) - =========== ================ ====================================== - - - ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object - you provided to PPO. - - seed (int): Seed for random number generators. - - steps_per_epoch (int): Number of steps of interaction (state-action pairs) - for the agent and the environment in each epoch. - - epochs (int): Number of epochs of interaction (equivalent to - number of policy updates) to perform. - - gamma (float): Discount factor. (Always between 0 and 1.) - - clip_ratio (float): Hyperparameter for clipping in the policy objective. - Roughly: how far can the new policy go from the old policy while - still profiting (improving the objective function)? The new policy - can still go farther than the clip_ratio says, but it doesn't help - on the objective anymore. (Usually small, 0.1 to 0.3.) Typically - denoted by :math:`\epsilon`. - - pi_lr (float): Learning rate for policy optimizer. - - vf_lr (float): Learning rate for value function optimizer. - - train_pi_iters (int): Maximum number of gradient descent steps to take - on policy loss per epoch. (Early stopping may cause optimizer - to take fewer than this.) - - train_v_iters (int): Number of gradient descent steps to take on - value function per epoch. - - lam (float): Lambda for GAE-Lambda. (Always between 0 and 1, - close to 1.) - - max_ep_len (int): Maximum length of trajectory / episode / rollout. - - target_kl (float): Roughly what KL divergence we think is appropriate - between new and old policies after an update. This will get used - for early stopping. (Usually small, 0.01 or 0.05.) - - logger_kwargs (dict): Keyword args for EpochLogger. - - save_freq (int): How often (in terms of gap between epochs) to save - the current policy and value function. - - """ - - # Special function to avoid certain slowdowns from PyTorch + MPI combo. - # print('exected ppo') - setup_pytorch_for_mpi() - - # Set up logger and save configuration - logger = EpochLogger(**logger_kwargs) - logger.save_config(locals()) - - # Random seed - seed += 10000 * proc_id() - torch.manual_seed(seed) - np.random.seed(seed) - - # Instantiate environment - env = env_fn - - obs_dim = env.observation_space.shape - act_dim = env.action_space.shape - - ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs) - # ac = torch.load("logs/ppo-with-cnn/ppo-with-cnn_s0/pyt_save/model.pt") - - sync_params(ac) - - # Count variables - def count_vars(module): - return sum([np.prod(p.shape) for p in module.parameters()]) - - var_counts = tuple(count_vars(module) for module in [ac.pi, ac.v]) - logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' % var_counts) - - # Set up experience buffer - local_steps_per_epoch = int(steps_per_epoch / num_procs()) - buf = PPOBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam) - - # PPOBuffer(obs_dim, env.action_space.shape, local_steps_per_epoch, gamma, lam) - # Set up function for computing PPO policy loss - def compute_loss_pi(data): - obs, act, adv, logp_old = data['obs'], data['act'], data['adv'], data[ - 'logp'] - - # Policy loss - pi, logp = ac.pi(obs, act) - #print("pi={}".format(pi)) - ratio = torch.exp(logp - logp_old) - clip_adv = torch.clamp(ratio, 1 - clip_ratio, 1 + clip_ratio) * adv - loss_pi = -(torch.min(ratio * adv, clip_adv)).mean() - - # Useful extra info - approx_kl = (logp_old - logp).mean().item() - ent = pi.entropy().mean().item() - clipped = ratio.gt(1 + clip_ratio) | ratio.lt(1 - clip_ratio) - clipfrac = torch.as_tensor(clipped, dtype=torch.float32).mean().item() - pi_info = dict(kl=approx_kl, ent=ent, cf=clipfrac) - - return loss_pi, pi_info - - # Set up function for computing value loss - def compute_loss_v(data): - obs, ret = data['obs'], data['ret'] - return ((ac.v(obs) - ret)**2).mean() - - # Set up optimizers for policy and value function - pi_optimizer = Adam(ac.pi.parameters(), lr=pi_lr) - vf_optimizer = Adam(ac.v.parameters(), lr=vf_lr) - - # Set up model saving - logger.setup_pytorch_saver(ac) - - def update(): - data = buf.get() - - pi_l_old, pi_info_old = compute_loss_pi(data) - pi_l_old = pi_l_old.item() - v_l_old = compute_loss_v(data).item() - - # Train policy with multiple steps of gradient descent - for i in range(train_pi_iters): - pi_optimizer.zero_grad() - loss_pi, pi_info = compute_loss_pi(data) - kl = mpi_avg(pi_info['kl']) - if kl > 1.5 * target_kl: - logger.log( - 'Early stopping at step %d due to reaching max kl.' % i) - break - loss_pi.backward() - mpi_avg_grads(ac.pi) # average grads across MPI processes - pi_optimizer.step() - - logger.store(StopIter=i) - - # Value function learning - for i in range(train_v_iters): - vf_optimizer.zero_grad() - loss_v = compute_loss_v(data) - loss_v.backward() - mpi_avg_grads(ac.v) # average grads across MPI processes - vf_optimizer.step() - - # Log changes from update - kl, ent, cf = pi_info['kl'], pi_info_old['ent'], pi_info['cf'] - logger.store(LossPi=pi_l_old, - LossV=v_l_old, - KL=kl, - Entropy=ent, - ClipFrac=cf, - DeltaLossPi=(loss_pi.item() - pi_l_old), - DeltaLossV=(loss_v.item() - v_l_old)) - - # Prepare for interaction with environment - start_time = time.time() - o, ep_ret, ep_len = env.reset(), 0, 0 - - # Main loop: collect experience in env and update/log each epoch - for epoch in range(epochs): - - for t in range(local_steps_per_epoch): - - a, v, logp = ac.step(torch.as_tensor(o, dtype=torch.float32)) - - # print(Back.RED+'a={}'.format(a)) - next_o, r, d, _ = env.step(a) - ep_ret += r - ep_len += 1 - - buf.store(o, a, r, v, logp) - logger.store(VVals=v) - - # Update obs (critical!) - o = next_o - - timeout = ep_len == max_ep_len - terminal = d or timeout - epoch_ended = t == local_steps_per_epoch - 1 - - if terminal or epoch_ended: - if epoch_ended and not (terminal): - print('Warning: trajectory cut off by epoch at %d steps.' % - ep_len, - flush=True) - - if timeout or epoch_ended: - _, v, _ = ac.step(torch.as_tensor(o, dtype=torch.float32)) - else: - v = 0 - - buf.finish_path(v) - if terminal: - # only save EpRet / EpLen if trajectory finished - logger.store(EpRet=ep_ret, EpLen=ep_len) - print(Fore.GREEN + - 'ep_ret={}, ep_len={}'.format(ep_ret, ep_len)) - o, ep_ret, ep_len = env.reset(), 0, 0 - - # Save model - if (epoch % save_freq == 0) or (epoch == epochs - 1): - logger.save_state({'env': env}, None) - - # Perform PPO update! - update() - - # Log info about epoch - logger.log_tabular('Epoch', epoch) - logger.log_tabular('EpRet', with_min_and_max=True) - logger.log_tabular('EpLen', average_only=True) - logger.log_tabular('VVals', with_min_and_max=True) - logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch) - logger.log_tabular('LossPi', average_only=True) - logger.log_tabular('LossV', average_only=True) - logger.log_tabular('DeltaLossPi', average_only=True) - logger.log_tabular('DeltaLossV', average_only=True) - logger.log_tabular('Entropy', average_only=True) - logger.log_tabular('KL', average_only=True) - logger.log_tabular('ClipFrac', average_only=True) - logger.log_tabular('StopIter', average_only=True) - logger.log_tabular('Time', time.time() - start_time) - logger.dump_tabular() - - -if __name__ == '__main__': - import argparse - parser = argparse.ArgumentParser() - parser.add_argument('--env', type=str, - default='LunarLander-v2') #'HalfCheetah-v2') - parser.add_argument('--hid', type=int, default=64) - parser.add_argument('--l', type=int, default=2) - parser.add_argument('--gamma', type=float, default=0.999) - parser.add_argument('--seed', '-s', type=int, default=0) - parser.add_argument('--cpu', type=int, default=1) - parser.add_argument('--steps', type=int, default=4000) - parser.add_argument('--epochs', type=int, default=350) - #parser.add_argument('--pretrain', type=str, default='/root/lele/spinningup/spinningup/data/ppo_0715/ppo_0715_s0/pyt_save/model.pt') - parser.add_argument('--exp_name', type=str, default='ppo-cnn-kuka-reach') - parser.add_argument('--log_dir', type=str, default="../logs") - args = parser.parse_args() - - mpi_fork(args.cpu) # run parallel code with mpi - - from spinup.utils.run_utils import setup_logger_kwargs - logger_kwargs = setup_logger_kwargs(args.exp_name, - args.seed, - data_dir=args.log_dir) - - env_fn = lambda: create_train_env(1, 1, 'complex') - - ppo( - env_fn, - actor=userActor, - critic=userCritic, #core.MLPActorCritic, #gym.make(args.env) - ac_kwargs=dict(hidden_sizes=[args.hid] * args.l), - gamma=args.gamma, - seed=args.seed, - steps_per_epoch=args.steps, - epochs=args.epochs, - logger_kwargs=logger_kwargs, - clip_ratio=0.2, - pi_lr=0.001, - vf_lr=0.001, - pretrain=None) diff --git a/ppo/ppo_cnn_cuda.py b/ppo/ppo_cnn_cuda.py deleted file mode 100644 index c7b7620..0000000 --- a/ppo/ppo_cnn_cuda.py +++ /dev/null @@ -1,476 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -from torch.optim import Adam -import gym -import time -import scipy.signal - -from ppo.core_cnn_cuda import CNNActorCritic -import ppo.core_cnn_cuda as core - -from ppo.logx import EpochLogger -from spinup.utils.mpi_pytorch import setup_pytorch_for_mpi, sync_params, mpi_avg_grads -from spinup.utils.mpi_tools import mpi_fork, mpi_avg, proc_id, mpi_statistics_scalar, num_procs -from colorama import Fore, Back, init - -init(autoreset=True) - -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - - -def combined_shape(length, shape=None): - if shape is None: - return (length, ) - return (length, shape) if np.isscalar(shape) else (length, *shape) - - -def discount_cumsum(x, discount): - """ - magic from rllab for computing discounted cumulative sums of vectors. - - input: - vector x, - [x0, - x1, - x2] - - output: - [x0 + discount * x1 + discount^2 * x2, - x1 + discount * x2, - x2] - """ - return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], - axis=0)[::-1] - - -class PPOBuffer: - """ - A buffer for storing trajectories experienced by a PPO agent interacting - with the environment, and using Generalized Advantage Estimation (GAE-Lambda) - for calculating the advantages of state-action pairs. - """ - def __init__(self, obs_dim, act_dim, size, gamma=0.99, lam=0.95): - self.obs_buf = np.zeros(combined_shape(size, obs_dim), - dtype=np.float32) - self.act_buf = np.zeros(combined_shape(size, act_dim), - dtype=np.float32) - self.adv_buf = np.zeros(size, dtype=np.float32) - self.rew_buf = np.zeros(size, dtype=np.float32) - self.ret_buf = np.zeros(size, dtype=np.float32) - self.val_buf = np.zeros(size, dtype=np.float32) - self.logp_buf = np.zeros(size, dtype=np.float32) - self.gamma, self.lam = gamma, lam - self.ptr, self.path_start_idx, self.max_size = 0, 0, size - - def store(self, obs, act, rew, val, logp): - """ - Append one timestep of agent-environment interaction to the buffer. - """ - assert self.ptr < self.max_size # buffer has to have room so you can store - self.obs_buf[self.ptr] = obs - self.act_buf[self.ptr] = act - self.rew_buf[self.ptr] = rew - self.val_buf[self.ptr] = val - self.logp_buf[self.ptr] = logp - self.ptr += 1 - - def finish_path(self, last_val=0): - """ - Call this at the end of a trajectory, or when one gets cut off - by an epoch ending. This looks back in the buffer to where the - trajectory started, and uses rewards and value estimates from - the whole trajectory to compute advantage estimates with GAE-Lambda, - as well as compute the rewards-to-go for each state, to use as - the targets for the value function. - - The "last_val" argument should be 0 if the trajectory ended - because the agent reached a terminal state (died), and otherwise - should be V(s_T), the value function estimated for the last state. - This allows us to bootstrap the reward-to-go calculation to account - for timesteps beyond the arbitrary episode horizon (or epoch cutoff). - """ - - path_slice = slice(self.path_start_idx, self.ptr) - rews = np.append(self.rew_buf[path_slice], last_val) - vals = np.append(self.val_buf[path_slice], last_val) - - # the next two lines implement GAE-Lambda advantage calculation - deltas = rews[:-1] + self.gamma * vals[1:] - vals[:-1] - self.adv_buf[path_slice] = discount_cumsum(deltas, - self.gamma * self.lam) - - # the next line computes rewards-to-go, to be targets for the value function - self.ret_buf[path_slice] = discount_cumsum(rews, self.gamma)[:-1] - - self.path_start_idx = self.ptr - - def get(self): - """ - Call this at the end of an epoch to get all of the data from - the buffer, with advantages appropriately normalized (shifted to have - mean zero and std one). Also, resets some pointers in the buffer. - """ - assert self.ptr == self.max_size # buffer has to be full before you can get - self.ptr, self.path_start_idx = 0, 0 - # the next two lines implement the advantage normalization trick - adv_mean, adv_std = mpi_statistics_scalar(self.adv_buf) - self.adv_buf = (self.adv_buf - adv_mean) / adv_std - data = dict(obs=self.obs_buf, - act=self.act_buf, - ret=self.ret_buf, - adv=self.adv_buf, - logp=self.logp_buf) - #data.to(device) - return { - k: torch.as_tensor(v, dtype=torch.float32).to(device) - for k, v in data.items() - } - - -def ppo(env_fn, - actor_critic=core.CNNActorCritic, - ac_kwargs=dict(), - seed=0, - steps_per_epoch=4000, - epochs=50, - gamma=0.99, - clip_ratio=0.2, - pi_lr=3e-4, - vf_lr=1e-3, - train_pi_iters=80, - train_v_iters=80, - lam=0.97, - max_ep_len=1000, - target_kl=0.01, - logger_kwargs=dict(), - save_freq=10, - pretrain=None): - """ - Proximal Policy Optimization (by clipping), - - with early stopping based on approximate KL - - Args: - env_fn : A function which creates a copy of the environment. - The environment must satisfy the OpenAI Gym API. - - actor_critic: The constructor method for a PyTorch Module with a - ``step`` method, an ``act`` method, a ``pi`` module, and a ``v`` - module. The ``step`` method should accept a batch of observations - and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``a`` (batch, act_dim) | Numpy array of actions for each - | observation. - ``v`` (batch,) | Numpy array of value estimates - | for the provided observations. - ``logp_a`` (batch,) | Numpy array of log probs for the - | actions in ``a``. - =========== ================ ====================================== - - The ``act`` method behaves the same as ``step`` but only returns ``a``. - - The ``pi`` module's forward call should accept a batch of - observations and optionally a batch of actions, and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``pi`` N/A | Torch Distribution object, containing - | a batch of distributions describing - | the policy for the provided observations. - ``logp_a`` (batch,) | Optional (only returned if batch of - | actions is given). Tensor containing - | the log probability, according to - | the policy, of the provided actions. - | If actions not given, will contain - | ``None``. - =========== ================ ====================================== - - The ``v`` module's forward call should accept a batch of observations - and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``v`` (batch,) | Tensor containing the value estimates - | for the provided observations. (Critical: - | make sure to flatten this!) - =========== ================ ====================================== - - - ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object - you provided to PPO. - - seed (int): Seed for random number generators. - - steps_per_epoch (int): Number of steps of interaction (state-action pairs) - for the agent and the environment in each epoch. - - epochs (int): Number of epochs of interaction (equivalent to - number of policy updates) to perform. - - gamma (float): Discount factor. (Always between 0 and 1.) - - clip_ratio (float): Hyperparameter for clipping in the policy objective. - Roughly: how far can the new policy go from the old policy while - still profiting (improving the objective function)? The new policy - can still go farther than the clip_ratio says, but it doesn't help - on the objective anymore. (Usually small, 0.1 to 0.3.) Typically - denoted by :math:`\epsilon`. - - pi_lr (float): Learning rate for policy optimizer. - - vf_lr (float): Learning rate for value function optimizer. - - train_pi_iters (int): Maximum number of gradient descent steps to take - on policy loss per epoch. (Early stopping may cause optimizer - to take fewer than this.) - - train_v_iters (int): Number of gradient descent steps to take on - value function per epoch. - - lam (float): Lambda for GAE-Lambda. (Always between 0 and 1, - close to 1.) - - max_ep_len (int): Maximum length of trajectory / episode / rollout. - - target_kl (float): Roughly what KL divergence we think is appropriate - between new and old policies after an update. This will get used - for early stopping. (Usually small, 0.01 or 0.05.) - - logger_kwargs (dict): Keyword args for EpochLogger. - - save_freq (int): How often (in terms of gap between epochs) to save - the current policy and value function. - - """ - - # Special function to avoid certain slowdowns from PyTorch + MPI combo. - #print('exected ppo') - setup_pytorch_for_mpi() - - # Set up logger and save configuration - logger = EpochLogger(**logger_kwargs) - logger.save_config(locals()) - - # Random seed - seed += 10000 * proc_id() - torch.manual_seed(seed) - np.random.seed(seed) - - # Instantiate environment - env = env_fn - - obs_dim = env.observation_space.shape - act_dim = env.action_space.shape - - #ac=actor_critic(env.observation_space,env.action_space,**ac_kwargs) - - ac = actor_critic(env.observation_space, env.action_space, **ac_kwargs) - - ac.to(device) - - # Count variables - def count_vars(module): - return sum([np.prod(p.shape) for p in module.parameters()]) - - var_counts = tuple(count_vars(module) for module in [ac.pi, ac.v]) - logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n' % var_counts) - - # Set up experience buffer - local_steps_per_epoch = int(steps_per_epoch / num_procs()) - buf = PPOBuffer(obs_dim, act_dim, local_steps_per_epoch, gamma, lam) - - # PPOBuffer(obs_dim, env.action_space.shape, local_steps_per_epoch, gamma, lam) - # Set up function for computing PPO policy loss - def compute_loss_pi(data): - obs, act, adv, logp_old = data['obs'], data['act'], data['adv'], data[ - 'logp'] - - # Policy loss - pi, logp = ac.pi(obs, act) - #print("pi={}".format(pi)) - ratio = torch.exp(logp - logp_old) - clip_adv = torch.clamp(ratio, 1 - clip_ratio, 1 + clip_ratio) * adv - loss_pi = -(torch.min(ratio * adv, clip_adv)).mean() - - # Useful extra info - approx_kl = (logp_old - logp).mean().item() - ent = pi.entropy().mean().item() - clipped = ratio.gt(1 + clip_ratio) | ratio.lt(1 - clip_ratio) - clipfrac = torch.as_tensor(clipped, dtype=torch.float32).mean().item() - pi_info = dict(kl=approx_kl, ent=ent, cf=clipfrac) - - return loss_pi, pi_info - - # Set up function for computing value loss - def compute_loss_v(data): - obs, ret = data['obs'], data['ret'] - return ((ac.v(obs) - ret)**2).mean() - - # Set up optimizers for policy and value function - pi_optimizer = Adam(ac.pi.parameters(), lr=pi_lr) - vf_optimizer = Adam(ac.v.parameters(), lr=vf_lr) - - # Set up model saving - logger.setup_pytorch_saver(ac) - - def update(): - data = buf.get() - - pi_l_old, pi_info_old = compute_loss_pi(data) - pi_l_old = pi_l_old.item() - v_l_old = compute_loss_v(data).item() - - # Train policy with multiple steps of gradient descent - for i in range(train_pi_iters): - pi_optimizer.zero_grad() - loss_pi, pi_info = compute_loss_pi(data) - kl = mpi_avg(pi_info['kl']) - if kl > 1.5 * target_kl: - logger.log( - 'Early stopping at step %d due to reaching max kl.' % i) - break - loss_pi.backward() - #mpi_avg_grads(ac.pi) # average grads across MPI processes - pi_optimizer.step() - - logger.store(StopIter=i) - - # Value function learning - for i in range(train_v_iters): - vf_optimizer.zero_grad() - loss_v = compute_loss_v(data) - loss_v.backward() - #mpi_avg_grads(ac.v) # average grads across MPI processes - vf_optimizer.step() - - # Log changes from update - kl, ent, cf = pi_info['kl'], pi_info_old['ent'], pi_info['cf'] - logger.store(LossPi=pi_l_old, - LossV=v_l_old, - KL=kl, - Entropy=ent, - ClipFrac=cf, - DeltaLossPi=(loss_pi.item() - pi_l_old), - DeltaLossV=(loss_v.item() - v_l_old)) - - # Prepare for interaction with environment - start_time = time.time() - o, ep_ret, ep_len = env.reset(), 0, 0 - - # Main loop: collect experience in env and update/log each epoch - for epoch in range(epochs): - - for t in range(local_steps_per_epoch): - - a, v, logp = ac.step( - torch.as_tensor(o, dtype=torch.float32).to(device)) - - # print(Back.RED+'a={}'.format(a)) - next_o, r, d, _ = env.step(a.detach().cpu().numpy()) - ep_ret += r - ep_len += 1 - - buf.store(o, - a.detach().cpu().numpy(), r, - v.detach().cpu().numpy().item(), - logp.detach().cpu().numpy().item()) - logger.store(VVals=v.detach().cpu().numpy().item()) - - # Update obs (critical!) - o = next_o - - timeout = ep_len == max_ep_len - terminal = d or timeout - epoch_ended = t == local_steps_per_epoch - 1 - - if terminal or epoch_ended: - if epoch_ended and not (terminal): - print('Warning: trajectory cut off by epoch at %d steps.' % - ep_len, - flush=True) - - if timeout or epoch_ended: - _, v, _ = ac.step(torch.as_tensor(o, dtype=torch.float32).to(device)) - v=v.detach().cpu().numpy().item() - else: - v = 0 - - buf.finish_path(v) - if terminal: - # only save EpRet / EpLen if trajectory finished - logger.store(EpRet=ep_ret, EpLen=ep_len) - print(Fore.GREEN + - 'ep_ret={}, ep_len={}'.format(ep_ret, ep_len)) - o, ep_ret, ep_len = env.reset(), 0, 0 - - # Save model - if (epoch % save_freq == 0) or (epoch == epochs - 1): - logger.save_state({'env': env}, None) - - # Perform PPO update! - update() - - # Log info about epoch - logger.log_tabular('Epoch', epoch) - logger.log_tabular('EpRet', with_min_and_max=True) - logger.log_tabular('EpLen', average_only=True) - logger.log_tabular('VVals', with_min_and_max=True) - logger.log_tabular('TotalEnvInteracts', (epoch + 1) * steps_per_epoch) - logger.log_tabular('LossPi', average_only=True) - logger.log_tabular('LossV', average_only=True) - logger.log_tabular('DeltaLossPi', average_only=True) - logger.log_tabular('DeltaLossV', average_only=True) - logger.log_tabular('Entropy', average_only=True) - logger.log_tabular('KL', average_only=True) - logger.log_tabular('ClipFrac', average_only=True) - logger.log_tabular('StopIter', average_only=True) - logger.log_tabular('Time', time.time() - start_time) - logger.dump_tabular() - - -if __name__ == '__main__': - import argparse - parser = argparse.ArgumentParser() - parser.add_argument('--env', type=str, - default='LunarLander-v2') #'HalfCheetah-v2') - parser.add_argument('--hid', type=int, default=64) - parser.add_argument('--l', type=int, default=2) - parser.add_argument('--gamma', type=float, default=0.999) - parser.add_argument('--seed', '-s', type=int, default=0) - parser.add_argument('--cpu', type=int, default=1) - parser.add_argument('--steps', type=int, default=4000) - parser.add_argument('--epochs', type=int, default=350) - #parser.add_argument('--pretrain', type=str, default='/root/lele/spinningup/spinningup/data/ppo_0715/ppo_0715_s0/pyt_save/model.pt') - parser.add_argument('--exp_name', type=str, default='ppo-cnn-kuka-reach') - parser.add_argument('--log_dir', type=str, default="../logs") - args = parser.parse_args() - - mpi_fork(args.cpu) # run parallel code with mpi - - from spinup.utils.run_utils import setup_logger_kwargs - logger_kwargs = setup_logger_kwargs(args.exp_name, - args.seed, - data_dir=args.log_dir) - - env_fn = lambda: create_train_env(1, 1, 'complex') - - ppo( - env_fn, - actor=userActor, - critic=userCritic, #core.MLPActorCritic, #gym.make(args.env) - ac_kwargs=dict(hidden_sizes=[args.hid] * args.l), - gamma=args.gamma, - seed=args.seed, - steps_per_epoch=args.steps, - epochs=args.epochs, - logger_kwargs=logger_kwargs, - clip_ratio=0.2, - pi_lr=0.001, - vf_lr=0.001, - pretrain=None) diff --git a/ppo/ppo_lstm.py b/ppo/ppo_lstm.py deleted file mode 100644 index 6d56143..0000000 --- a/ppo/ppo_lstm.py +++ /dev/null @@ -1,464 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -from torch.optim import Adam -import gym -import time -import scipy.signal -# import spinup.algos.pytorch.ppo.core as core -# from core_1 import Actor, Critic -from ppo.core_lstm import userCritic, userActor -#from env import create_train_env -from spinup.utils.logx import EpochLogger -from spinup.utils.mpi_pytorch import setup_pytorch_for_mpi, sync_params, mpi_avg_grads -from spinup.utils.mpi_tools import mpi_fork, mpi_avg, proc_id, mpi_statistics_scalar, num_procs -from colorama import Fore,Back,init -init(autoreset=True) -#device = torch.device('cuda') -device=torch.device("cuda" if torch.cuda.is_available() else "cpu") - -def combined_shape(length, shape=None): - if shape is None: - return (length,) - return (length, shape) if np.isscalar(shape) else (length, *shape) - -def discount_cumsum(x, discount): - """ - magic from rllab for computing discounted cumulative sums of vectors. - - input: - vector x, - [x0, - x1, - x2] - - output: - [x0 + discount * x1 + discount^2 * x2, - x1 + discount * x2, - x2] - """ - return scipy.signal.lfilter([1], [1, float(-discount)], x[::-1], axis=0)[::-1] - -class PPOBuffer: - """ - A buffer for storing trajectories experienced by a PPO agent interacting - with the environment, and using Generalized Advantage Estimation (GAE-Lambda) - for calculating the advantages of state-action pairs. - """ - - def __init__(self, obs_dim, act_dim, hidden_dim, size, gamma=0.99, lam=0.95): - self.obs_buf = np.zeros(combined_shape(size, obs_dim), dtype=np.float32) - self.act_buf = np.zeros(combined_shape(size, act_dim), dtype=np.float32) - self.adv_buf = np.zeros(size, dtype=np.float32) - self.rew_buf = np.zeros(size, dtype=np.float32) - self.ret_buf = np.zeros(size, dtype=np.float32) - self.val_buf = np.zeros(size, dtype=np.float32) - self.logp_buf = np.zeros(size, dtype=np.float32) - self.hidden_h_buf = np.zeros(combined_shape(size, hidden_dim), dtype=np.float32) - self.hidden_c_buf = np.zeros(combined_shape(size, hidden_dim), dtype=np.float32) - #print(self.hidden_h_buf.shape) - self.gamma, self.lam = gamma, lam - self.ptr, self.path_start_idx, self.max_size = 0, 0, size - - def store(self, obs, act, rew, val, logp, hidden_h, hidden_c): - """ - Append one timestep of agent-environment interaction to the buffer. - """ - assert self.ptr < self.max_size # buffer has to have room so you can store - self.obs_buf[self.ptr] = obs - self.act_buf[self.ptr] = act - self.rew_buf[self.ptr] = rew - self.val_buf[self.ptr] = val - self.logp_buf[self.ptr] = logp - self.hidden_h_buf[self.ptr] = hidden_h - self.hidden_c_buf[self.ptr] = hidden_c - self.ptr += 1 - - def finish_path(self, last_val=0): - """ - Call this at the end of a trajectory, or when one gets cut off - by an epoch ending. This looks back in the buffer to where the - trajectory started, and uses rewards and value estimates from - the whole trajectory to compute advantage estimates with GAE-Lambda, - as well as compute the rewards-to-go for each state, to use as - the targets for the value function. - - The "last_val" argument should be 0 if the trajectory ended - because the agent reached a terminal state (died), and otherwise - should be V(s_T), the value function estimated for the last state. - This allows us to bootstrap the reward-to-go calculation to account - for timesteps beyond the arbitrary episode horizon (or epoch cutoff). - """ - - path_slice = slice(self.path_start_idx, self.ptr) - rews = np.append(self.rew_buf[path_slice], last_val) - vals = np.append(self.val_buf[path_slice], last_val) - - # the next two lines implement GAE-Lambda advantage calculation - deltas = rews[:-1] + self.gamma * vals[1:] - vals[:-1] - self.adv_buf[path_slice] = discount_cumsum(deltas, self.gamma * self.lam) - - # the next line computes rewards-to-go, to be targets for the value function - self.ret_buf[path_slice] = discount_cumsum(rews, self.gamma)[:-1] - - self.path_start_idx = self.ptr - - def get(self): - """ - Call this at the end of an epoch to get all of the data from - the buffer, with advantages appropriately normalized (shifted to have - mean zero and std one). Also, resets some pointers in the buffer. - """ - assert self.ptr == self.max_size # buffer has to be full before you can get - self.ptr, self.path_start_idx = 0, 0 - # the next two lines implement the advantage normalization trick - adv_mean, adv_std = mpi_statistics_scalar(self.adv_buf) - self.adv_buf = (self.adv_buf - adv_mean) / adv_std - data = dict(obs=self.obs_buf, act=self.act_buf, ret=self.ret_buf, - adv=self.adv_buf, logp=self.logp_buf, hidden_h=self.hidden_h_buf, hidden_c=self.hidden_c_buf) - #data.to(device) - return {k: torch.as_tensor(v, dtype=torch.float32).to(device) for k,v in data.items()} - - - -def ppo(env_fn, actor=nn.Module, critic=nn.Module, ac_kwargs=dict(), seed=0, - steps_per_epoch=4000, epochs=50, gamma=0.99, clip_ratio=0.2, pi_lr=3e-4, - vf_lr=1e-3, train_pi_iters=80, train_v_iters=80, lam=0.97, max_ep_len=1000, - target_kl=0.01, logger_kwargs=dict(), save_freq=10, pretrain=None): - """ - Proximal Policy Optimization (by clipping), - - with early stopping based on approximate KL - - Args: - env_fn : A function which creates a copy of the environment. - The environment must satisfy the OpenAI Gym API. - - actor_critic: The constructor method for a PyTorch Module with a - ``step`` method, an ``act`` method, a ``pi`` module, and a ``v`` - module. The ``step`` method should accept a batch of observations - and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``a`` (batch, act_dim) | Numpy array of actions for each - | observation. - ``v`` (batch,) | Numpy array of value estimates - | for the provided observations. - ``logp_a`` (batch,) | Numpy array of log probs for the - | actions in ``a``. - =========== ================ ====================================== - - The ``act`` method behaves the same as ``step`` but only returns ``a``. - - The ``pi`` module's forward call should accept a batch of - observations and optionally a batch of actions, and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``pi`` N/A | Torch Distribution object, containing - | a batch of distributions describing - | the policy for the provided observations. - ``logp_a`` (batch,) | Optional (only returned if batch of - | actions is given). Tensor containing - | the log probability, according to - | the policy, of the provided actions. - | If actions not given, will contain - | ``None``. - =========== ================ ====================================== - - The ``v`` module's forward call should accept a batch of observations - and return: - - =========== ================ ====================================== - Symbol Shape Description - =========== ================ ====================================== - ``v`` (batch,) | Tensor containing the value estimates - | for the provided observations. (Critical: - | make sure to flatten this!) - =========== ================ ====================================== - - - ac_kwargs (dict): Any kwargs appropriate for the ActorCritic object - you provided to PPO. - - seed (int): Seed for random number generators. - - steps_per_epoch (int): Number of steps of interaction (state-action pairs) - for the agent and the environment in each epoch. - - epochs (int): Number of epochs of interaction (equivalent to - number of policy updates) to perform. - - gamma (float): Discount factor. (Always between 0 and 1.) - - clip_ratio (float): Hyperparameter for clipping in the policy objective. - Roughly: how far can the new policy go from the old policy while - still profiting (improving the objective function)? The new policy - can still go farther than the clip_ratio says, but it doesn't help - on the objective anymore. (Usually small, 0.1 to 0.3.) Typically - denoted by :math:`\epsilon`. - - pi_lr (float): Learning rate for policy optimizer. - - vf_lr (float): Learning rate for value function optimizer. - - train_pi_iters (int): Maximum number of gradient descent steps to take - on policy loss per epoch. (Early stopping may cause optimizer - to take fewer than this.) - - train_v_iters (int): Number of gradient descent steps to take on - value function per epoch. - - lam (float): Lambda for GAE-Lambda. (Always between 0 and 1, - close to 1.) - - max_ep_len (int): Maximum length of trajectory / episode / rollout. - - target_kl (float): Roughly what KL divergence we think is appropriate - between new and old policies after an update. This will get used - for early stopping. (Usually small, 0.01 or 0.05.) - - logger_kwargs (dict): Keyword args for EpochLogger. - - save_freq (int): How often (in terms of gap between epochs) to save - the current policy and value function. - - """ - - # Special function to avoid certain slowdowns from PyTorch + MPI combo. - #print('exected ppo') - setup_pytorch_for_mpi() - - # Set up logger and save configuration - logger = EpochLogger(**logger_kwargs) - logger.save_config(locals()) - - # Random seed - seed += 10000 * proc_id() - torch.manual_seed(seed) - np.random.seed(seed) - - # Instantiate environment - env = env_fn - # obs_dim = env.observation_space.shape - # act_dim = env.action_space.shape - obs_dim = env.observation_space.shape - act_dim = env.action_space.shape - # Create actor-critic module - # ac_pi = actor(np.prod(obs_dim), act_dim, hidden_sizes=[64,64], activation=nn.Tanh)#env.observation_space, env.action_space, nn.ReLU) - # ac_v = critic(np.prod(obs_dim), hidden_sizes=[64,64], activation=nn.Tanh)#env.observation_space, nn.ReLU) - if pretrain != None: - ac_pi = torch.load(pretrain) - else: - ac_pi = actor(obs_dim[0], act_dim[0], hidden_sizes=[64, 64], activation=nn.Tanh, pretrain=pretrain) # env.observation_space, env.action_space, nn.ReLU) - ac_v = critic(obs_dim[0], hidden_sizes=[64, 64], activation=nn.Tanh) # env.observation_space, nn.ReLU) - - ac_pi.to(device) - ac_v.to(device) - #ac_pi = nn.DataParallel(ac_pi) - #ac_v = nn.DataParallel(ac_v) - - # Sync params across processes - # sync_params(ac_pi) - # sync_params(ac_v) - - # Count variables - def count_vars(module): - return sum([np.prod(p.shape) for p in module.parameters()]) - var_counts = tuple(count_vars(module) for module in [ac_pi, ac_v]) - logger.log('\nNumber of parameters: \t pi: %d, \t v: %d\n'%var_counts) - - # Set up experience buffer - local_steps_per_epoch = int(steps_per_epoch / num_procs()) - buf = PPOBuffer(obs_dim, env.action_space.shape, 512, local_steps_per_epoch, gamma, lam) - # PPOBuffer(obs_dim, env.action_space.shape, local_steps_per_epoch, gamma, lam) - # Set up function for computing PPO policy loss - def compute_loss_pi(data): - obs, act, adv, logp_old , hidden_h, hidden_c = data['obs'], data['act'], data['adv'], data['logp'], data['hidden_h'], data['hidden_c'] - - # Policy loss - pi, logp ,_ = ac_pi(obs, act, (hidden_h, hidden_c)) - #print("pi={}".format(pi)) - ratio = torch.exp(logp - logp_old) - clip_adv = torch.clamp(ratio, 1-clip_ratio, 1+clip_ratio) * adv - loss_pi = -(torch.min(ratio * adv, clip_adv)).mean() - - # Useful extra info - approx_kl = (logp_old - logp).mean().item() - ent = pi.entropy().mean().item() - clipped = ratio.gt(1+clip_ratio) | ratio.lt(1-clip_ratio) - clipfrac = torch.as_tensor(clipped, dtype=torch.float32).mean().item() - pi_info = dict(kl=approx_kl, ent=ent, cf=clipfrac) - - return loss_pi, pi_info - - # Set up function for computing value loss - def compute_loss_v(data): - obs, ret ,hidden_h, hidden_c = data['obs'], data['ret'], data['hidden_h'], data['hidden_c'] - return ((ac_v(obs, (hidden_h, hidden_c)) - ret)**2).mean() - - # Set up optimizers for policy and value function - pi_optimizer = Adam(ac_pi.parameters(), lr=pi_lr) - vf_optimizer = Adam(ac_v.parameters(), lr=vf_lr) - - # Set up model saving - logger.setup_pytorch_saver(ac_pi) - - def update(): - data = buf.get() - - pi_l_old, pi_info_old = compute_loss_pi(data) - pi_l_old = pi_l_old.item() - v_l_old = compute_loss_v(data).item() - - # Train policy with multiple steps of gradient descent - for i in range(train_pi_iters): - pi_optimizer.zero_grad() - loss_pi, pi_info = compute_loss_pi(data) - kl = mpi_avg(pi_info['kl']) - if kl > 1.5 * target_kl: - logger.log('Early stopping at step %d due to reaching max kl.'%i) - break - loss_pi.backward() - mpi_avg_grads(ac_pi) # average grads across MPI processes - pi_optimizer.step() - - logger.store(StopIter=i) - - # Value function learning - for i in range(train_v_iters): - vf_optimizer.zero_grad() - loss_v = compute_loss_v(data) - loss_v.backward() - mpi_avg_grads(ac_v) # average grads across MPI processes - vf_optimizer.step() - - # Log changes from update - kl, ent, cf = pi_info['kl'], pi_info_old['ent'], pi_info['cf'] - logger.store(LossPi=pi_l_old, LossV=v_l_old, - KL=kl, Entropy=ent, ClipFrac=cf, - DeltaLossPi=(loss_pi.item() - pi_l_old), - DeltaLossV=(loss_v.item() - v_l_old)) - - # Prepare for interaction with environment - start_time = time.time() - o, ep_ret, ep_len = env.reset(), 0, 0 - - # Main loop: collect experience in env and update/log each epoch - for epoch in range(epochs): - hidden = (torch.zeros((1, 512), dtype=torch.float).to(device), torch.zeros((1, 512), dtype=torch.float).to(device)) - for t in range(local_steps_per_epoch): - # a, v, logp = ac.step(torch.as_tensor(o, dtype=torch.float32)) - with torch.no_grad(): - rr = torch.from_numpy(o.copy()).float().to(device)#.unsqueeze(0) - pi, _, hidden_ = ac_pi(rr, None, hidden) - a = pi.sample() - # logp_a = self.pi._log_prob_from_distribution(pi, a) - logp = pi.log_prob(a).sum(axis=-1) - v = ac_v(torch.as_tensor(o, dtype=torch.float32).to(device), hidden) - - #print('a={}'.format(a)) - a=a.cpu().detach().numpy()[0] - #print('a={}'.format(a)) - #print("a.cpu().numpy().item()={}".format(a.cpu().numpy().item())) - next_o, r, d, _ = env.step(a) - ep_ret += r - ep_len += 1 - #hidden = hidden_ - - # save and log - #print(hidden[0].shape) - #print('o.shape={}'.format(o.shape)) - o=o[0] - v=v.cpu().detach().numpy()[0] - logp=logp.cpu().detach().numpy()[0] - #print(Back.RED+'o={},\na={},\nr={},\nv={},\nlogp={}'.format(o,a,r,v,logp)) - buf.store(o, a, r, v, logp, hidden[0].cpu().numpy(), hidden[1].cpu().numpy()) - logger.store(VVals=v) - - # Update obs (critical!) - o = next_o - hidden = hidden_ - - timeout = ep_len == max_ep_len - terminal = d #or timeout - epoch_ended = t==local_steps_per_epoch-1 - - if terminal or epoch_ended: - if epoch_ended and not(terminal): - print('Warning: trajectory cut off by epoch at %d steps.'%ep_len, flush=True) - # if trajectory didn't reach terminal state, bootstrap value target - if epoch_ended: - print('epoch_end') - # _, v, _ = ac.step(torch.as_tensor(o, dtype=torch.float32)) - with torch.no_grad(): - v =ac_v(torch.from_numpy(o).float().to(device), hidden).cpu().numpy() - else: - print('epret :',ep_ret) - v = 0 - hidden= (torch.zeros((1, 512), dtype=torch.float).to(device), torch.zeros((1, 512), dtype=torch.float).to(device)) - buf.finish_path(v) - if terminal: - # only save EpRet / EpLen if trajectory finished - logger.store(EpRet=ep_ret, EpLen=ep_len) - o, ep_ret, ep_len = env.reset(), 0, 0 - - - # Save model - if (epoch % save_freq == 0) or (epoch == epochs-1): - logger.save_state({'env': env}, None) - - # Perform PPO update! - update() - - # Log info about epoch - logger.log_tabular('Epoch', epoch) - logger.log_tabular('EpRet', with_min_and_max=True) - logger.log_tabular('EpLen', average_only=True) - logger.log_tabular('VVals', with_min_and_max=True) - logger.log_tabular('TotalEnvInteracts', (epoch+1)*steps_per_epoch) - logger.log_tabular('LossPi', average_only=True) - logger.log_tabular('LossV', average_only=True) - logger.log_tabular('DeltaLossPi', average_only=True) - logger.log_tabular('DeltaLossV', average_only=True) - logger.log_tabular('Entropy', average_only=True) - logger.log_tabular('KL', average_only=True) - logger.log_tabular('ClipFrac', average_only=True) - logger.log_tabular('StopIter', average_only=True) - logger.log_tabular('Time', time.time()-start_time) - logger.dump_tabular() - -if __name__ == '__main__': - import argparse - parser = argparse.ArgumentParser() - parser.add_argument('--env', type=str, default='LunarLander-v2')#'HalfCheetah-v2') - parser.add_argument('--hid', type=int, default=64) - parser.add_argument('--l', type=int, default=2) - parser.add_argument('--gamma', type=float, default=0.999) - parser.add_argument('--seed', '-s', type=int, default=0) - parser.add_argument('--cpu', type=int, default=1) - parser.add_argument('--steps', type=int, default=4000) - parser.add_argument('--epochs', type=int, default=350) - parser.add_argument('--pretrain', type=str, default='/root/lele/spinningup/spinningup/data/ppo_0715/ppo_0715_s0/pyt_save/model.pt') - parser.add_argument('--exp_name', type=str, default='ppo_lstm_1106') - args = parser.parse_args() - - import os - #os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' - mpi_fork(args.cpu) # run parallel code with mpi - - from spinup.utils.run_utils import setup_logger_kwargs - logger_kwargs = setup_logger_kwargs(args.exp_name, args.seed) - # from baselines.common.vec_env.subproc_vec_env import SubprocVecEnv - env_fn = lambda : create_train_env(1,1,'complex') - # env_fn = SubprocVecEnv([]) - # env_fn = lambda : JoypadSpace(gym_super_mario_bros.make("SuperMarioBros-{}-{}-v0".format(1, 1)), gym_super_mario_bros.actions.COMPLEX_MOVEMENT) - #print('exec ppo') - ppo(env_fn, actor=userActor, critic=userCritic,#core.MLPActorCritic, #gym.make(args.env) - ac_kwargs=dict(hidden_sizes=[args.hid]*args.l), gamma=args.gamma, - seed=args.seed, steps_per_epoch=args.steps, epochs=args.epochs, - logger_kwargs=logger_kwargs, clip_ratio=0.2, pi_lr=0.001, vf_lr=0.001, pretrain=None) - From 5cf3f90e684325b62c3b87886d3be40e988907d9 Mon Sep 17 00:00:00 2001 From: borninfreedom Date: Wed, 28 Apr 2021 23:02:29 +0800 Subject: [PATCH 3/8] create wiki md files and clear readme.md --- README.md | 260 +-------------------------------- docs/Reinforcement Learning.md | 50 ++++++- docs/deep_learning.md | 4 + docs/pybullet.md | 8 + docs/python.md | 24 +++ docs/robotics.md | 18 +++ docs/vscode.md | 56 +++++++ 7 files changed, 166 insertions(+), 254 deletions(-) create mode 100644 docs/deep_learning.md create mode 100644 docs/pybullet.md create mode 100644 docs/python.md create mode 100644 docs/robotics.md create mode 100644 docs/vscode.md diff --git a/README.md b/README.md index b5d7599..37ca0b0 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # kuka-reach-drl Train kuka robot reach a point with deep rl in pybullet. - +* **NOTE: The main brach is trained with spinup, and there are some issues with gpu and multi core CPUs at the same time, so this brach will be deprecated in the future. The rllib branch is trained with ray/rllib, and this branch will be mainly used in the future.** The train process with mlp|The evaluate process with mlp|train plot :---------------:|:------------------:|:-------------------------: @@ -13,267 +13,23 @@ The train process with cnn|The evaluate process with cnn|train plot # Installation guide (Now only support linux and macos) -**I strongly recommend using Conda to install the env, because you will possible encounter the mpi4py error with pip.** - -The spinningup rl library is the necessary lib. -first, you should install miniconda or anaconda. -second, install some dev dependencies. +* I recommend using conda to install, because conda can help you install cudatoolkit and cudnn, so you don't need to care which version you have installed in your machine, conda helps you install them in an isolated environment. ```bash -sudo apt-get update && sudo apt-get install libopenmpi-dev -sudo apt install libgl1-mesa-glx +pip install ray,ray[rllib],ray[tune] +conda install pytorch-gpu +pip install pybullet ``` -third, create a conda virtual environment -```bash -conda create -n spinningup python=3.6 #python 3.6 is recommended -``` - - -```bash -#activate the env -conda activate spinningup -``` - -then, install spiningup,is contains almost dependencies -```bash -# clone my version, I made some changes. -git clone https://github.com/borninfreedom/spinningup.git -cd spinningup -pip install -e . -``` - -last, install torch and torchvision. - -if you have a gpu, please run this (conda will install a correct version of cudatoolkit and cudnn in the virtual env, so don't care which version you have installed in your machine.) -```bash -# CUDA 10.1 -conda install pytorch==1.4.0 torchvision==0.5.0 cudatoolkit=10.1 -c pytorch -``` - -if you only have a cpu, please run this, -```bash -# CPU Only -conda install pytorch==1.4.0 torchvision==0.5.0 cpuonly -c pytorch -``` - -## Alternative installation method -Or, you can create the virtual environment directly through -```bash -conda create --name spinningup --file requirements.txt -``` -but I can not ensure this method can success. - # Run instruction -if you want to train the kuka with coordition env, whose input to policy is the coordition of the target pos, and the actor critic framework is based on mlp, please run -```bash -python train_with_mlp.py --is_render --is_good_view --cpu 5 --epochs 100 -``` -if you don't want to view the scene, just train it, run -```bash -python train_with_mlp.py --cpu 5 --epochs 100 -``` - -if you want to train kuka with image input and cnn model,run -```bash -python train_with_cnn.py --is_render --is_good_view --cpu 5 --epochs 500 -``` -if you don't want to view the scene, just train it, run -```bash -python train_with_cnn.py --cpu 5 --epochs 500 -``` - - -if you want to train kuka with image input and lstm model,run -```bash -python train_with_lstm.py --is_render --is_good_view --cpu 5 --epochs 500 -``` -if you don't want to view the scene, just train it, run -```bash -python train_with_lstm.py --cpu 5 --epochs 500 -``` - - - - -# Files guide -the train.py file is the main train file, you can directly run it or through `python train.py --cpu 6` to run it in terminal. Please notice the parameters. - -eval.py file is the evaluate trained model file, the model is in the logs directory named model.pt. In the eval file, pybullet render is open default. **When you want to evaluate my trained model, please change the source code `ac=torch.load("logs/ppo-kuka-reach/ppo-kuka-reach_s0/pyt_save/model.pt")` to `ac=torch.load("saved_model/model.pt")` in `eval.py`** - -ppo directory is the main algorithms about ppo. - -env directory is the main pybullet env. - -## view the train results through plot -```bash -python -m spinup.run plot ./logs -``` -More detailed information please visit [plotting results](https://spinningup.openai.com/en/latest/user/plotting.html) - - -# Resources about deep rl reach and grasp. -## Articles -* [spinningup docs](https://spinningup.openai.com/en/latest/user/installation.html) -* [Proximal Policy Optimization Tutorial (Part 1/2: Actor-Critic Method)](https://towardsdatascience.com/proximal-policy-optimization-tutorial-part-1-actor-critic-method-d53f9afffbf6)(do not carefully read now.) -* [some ray/rllib and other rl problems' blogs](https://www.datahubbs.com/) -* [Action Masking with RLlib](https://towardsdatascience.com/action-masking-with-rllib-5e4bec5e7505) -* [This AI designs beautiful Forest Landscapes for Games!](https://medium.com/deepgamingai/this-ai-designs-beautiful-forest-landscapes-for-games-8675e053636e) -* [Chintan Trivedi's homepage](https://medium.com/@chintan.t93), he writes many blogs about AI and games. It's very recommended. -* [Proximal Policy Optimization Tutorial (Part 1/2: Actor-Critic Method)](https://twitter.com/ericwen5986/status/1374361315100172289) -* [Proximal Policy Optimization Tutorial (Part 2/2: GAE and PPO loss)](https://twitter.com/ericwen5986/status/1374361470859767809) -* [Antonin Raffin](https://araffin.github.io/), he is the member of stable baseline3 project. -* [spinningup using in pybullet envs](https://www.etedal.net/2020/04/pybullet-panda_3.html), this is a blog about how to use spinningup to pybullet envs and use the image as the observation. -* [Understanding LSTM Networks](https://colah.github.io/posts/2015-08-Understanding-LSTMs/), this is a good blog introducing lstm. - -## Source codes -* [robotics-rl-srl](https://github.com/araffin/robotics-rl-srl), S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics. In this project, there are CNN policy and instructions how to connect a real robot using deep rl. -* [zenetio/DeepRL-Robotic](https://github.com/zenetio/DeepRL-Robotic), a deep rl project using gazebo. -* [robotology-playground/pybullet-robot-envs](https://github.com/robotology-playground/pybullet-robot-envs), a deep rl project using pybullet, it is built by a company, there are a lot can study from their codes. But their envs do not introduce images. -* [mahyaret/kuka_rl](https://github.com/mahyaret/kuka_rl), a tutorial tells you how to implement DQN and ppo algorithms to kuka robot grasping. -* [AutodeskRoboticsLab/RLRoboticAssembly](https://github.com/AutodeskRoboticsLab/RLRoboticAssembly), a deep rl robot assembly project build by autodesk, it uses rllib and ray. -* [MorvanZhou/train-robot-arm-from-scratch](https://github.com/MorvanZhou/train-robot-arm-from-scratch), a deep rl robot project build by Morvan. -* **[BarisYazici/deep-rl-grasping](https://github.com/BarisYazici/deep-rl-grasping), a deep rl robot grasping project built by a student in Technical University of Munich. He also released his degree's paper, we can learn a lot from his paper.** -* [mahyaret/gym-panda](https://github.com/mahyaret/gym-panda), this is a pybullet panda environment for deep rl. In the codes, author makes the image as the observation. -* [gaoxiaos/Supermariobros-PPO-pytorch](https://github.com/gaoxiaos/Supermariobros-PPO-pytorch), a tutorial about how to implement deep rl to super mario game, the algorithms are modified from spiningup, and the observation is image. So the code is very suitable for image based deep rl. - -* [ShangtongZhang/reinforcement-learning-an-introduction](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction), this is the python version code of the book reinforcement learning an introduction second edition, the full book and other resources can be found here [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book-2nd.html). -# Machine learning and reinforcement learning knowledges -* [The Logit and Sigmoid Functions](https://nathanbrixius.wordpress.com/2016/06/04/functions-i-have-known-logit-and-sigmoid/) -* [Generalized Advantage Estimator](https://zhuanlan.zhihu.com/p/139097326) -* [Python浮点算术:争议和限制](https://docs.python.org/zh-cn/3/tutorial/floatingpoint.html) -* [rainbow-is-all-you-need](https://github.com/Curt-Park/rainbow-is-all-you-need) -* [pg-is-all-you-need](https://github.com/MrSyee/pg-is-all-you-need) +# Wiki +## [Reinforcement Learning]() +## [pybullet]() -# Robotics knowledge -* [回到基础——理解几何旋转与欧拉角](https://robodk.com/cn/blog/%E5%87%A0%E4%BD%95%E6%97%8B%E8%BD%AC%E4%B8%8E%E6%AC%A7%E6%8B%89%E8%A7%92/) -# Python Knowledge -* [Python中的作用域、global与nonlocal](https://note.qidong.name/2017/07/python-legb/) -* [Delgan/loguru](https://github.com/Delgan/loguru), this is a great python log module, it is much greater than python built in logging module. -* [wandb](https://wandb.ai/site),Developer tools for machine learning. Build better models faster with experiment tracking, dataset . -* logging usage -```python -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', - filename='./logs/client1-{}.log'.format(time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())), - filemode='w') -logger = logging.getLogger(__name__) -formatter = logging.Formatter('%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s') -stream_handler = logging.StreamHandler() - -stream_handler.setLevel(logging.INFO) -stream_handler.setFormatter(formatter) -logger.addHandler(stream_handler) - -# in the codes. -# logger.info() -# logger.debug() -``` - -# Blogs about deep rl written by me -1. [Ubuntu助手 — 一键自动安装软件,一键进行系统配置](https://www.guyuehome.com/15107) -2. [深度强化学习专栏 —— 1.研究现状](https://www.guyuehome.com/21403) -3. [深度强化学习专栏 —— 2.手撕DQN算法实现CartPole控制](https://www.guyuehome.com/22329) -4. [深度强化学习专栏 —— 3.实现一阶倒立摆](https://www.guyuehome.com/23439) -5. [深度强化学习专栏 —— 4. 使用ray做分布式计算](https://www.guyuehome.com/?p=26243) -6. [深度强化学习专栏 —— 5. 使用ray的tune组件优化强化学习算法的超参数](https://www.guyuehome.com/?p=26247) -7. [深度强化学习专栏 —— 6. 使用RLLib和ray进行强化学习训练](https://www.guyuehome.com/26251) -8. [深度强化学习专栏 —— 7. 实现机械臂reach某点之PPO算法实现(一)](https://www.guyuehome.com/33650) -9. [深度强化学习专栏 —— 8. 实现机械臂reach某点之PPO算法实现(二)](https://www.guyuehome.com/33663) -10. [深度强化学习专栏 —— 9. 实现机械臂reach某点之PPO算法实现(三)](https://www.guyuehome.com/33682) -11. [深度强化学习专栏 —— 10. 实现机械臂reach某点之环境实现实现](https://www.guyuehome.com/33691) - -# Blogs about pybullet written by me -1. [pybullet杂谈 :使用深度学习拟合相机坐标系与世界坐标系坐标变换关系(一)](https://www.guyuehome.com/24528) -2. [pybullet杂谈 :使用深度学习拟合相机坐标系与世界坐标系坐标变换关系(二)](https://www.guyuehome.com/26255) -3. [pybullet电机控制总结](https://blog.csdn.net/bornfree5511/article/details/108188632) -4. [Part 1 - 自定义gym环境](https://blog.csdn.net/bornfree5511/article/details/108212687) -5. [Part 1.1 - 注册自定义Gym环境](https://blog.csdn.net/bornfree5511/article/details/108212963) -6. [Part 1.2 - 实现一个井字棋游戏的gym环境](https://blog.csdn.net/bornfree5511/article/details/108214740) -7. [Part 1.3 - 熟悉PyBullet](https://blog.csdn.net/bornfree5511/article/details/108307638) -8. [Part 1.4 - 为PyBullet创建Gym环境](https://blog.csdn.net/bornfree5511/article/details/108326084) - -# Some resources about how to implement the RL to real robots -## Source codes -* [kindredresearch/SenseAct](https://github.com/kindredresearch/SenseAct) - -## Papers -* [Setting up a Reinforcement Learning Task with a Real-World Robot](https://arxiv.org/pdf/1803.07067.pdf) -* [Real-World Human-Robot Collaborative Reinforcement Learning](https://arxiv.org/pdf/2003.01156.pdf) - -## Blogs -* [入门机器人强化学习的一些坑:模拟环境篇](https://zhuanlan.zhihu.com/p/300541709) - -## Some comments from Facebook groups - 0|1|2|3 ---|--|--|-- -![](https://github.com/borninfreedom/kuka-reach-drl/blob/main/pictures/rosrl0.jpg)|![](https://github.com/borninfreedom/kuka-reach-drl/blob/main/pictures/rosrl1.jpg)|![](https://github.com/borninfreedom/kuka-reach-drl/blob/main/pictures/rosrl2.jpg)|![](https://github.com/borninfreedom/kuka-reach-drl/blob/main/pictures/rosrl3.jpg) - -# VSCode tricks -## [About python extensions](https://zhuanlan.zhihu.com/p/361654489?utm_source=com.miui.notes&utm_medium=social&utm_oi=903420714332332032) - -## Resolve a.py in A folder import b.py in B folder -* Add the codes below at the top of a .py file -```python -import os,inspect -current_dir=os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) -os.chdir(current_dir) -import sys -sys.path.append('../') -``` -## Add header template in .py files -* Select FIle -> Preference -> User Snippets -> 选择python文件 -* Add the codes below -```python - -{ - // Place your snippets for python here. Each snippet is defined under a snippet name and has a prefix, body and - // description. The prefix is what is used to trigger the snippet and the body will be expanded and inserted. Possible variables are: - // $1, $2 for tab stops, $0 for the final cursor position, and ${1:label}, ${2:another} for placeholders. Placeholders with the - // same ids are connected. - // Example: - // "Print to console": { - // "prefix": "log", - // "body": [ - // "console.log('$1');", - // "$2" - // ], - // "description": "Log output to console" - // } - - - - "HEADER":{ - "prefix": "header", - "body": [ - "#!/usr/bin/env python3", - "# -*- encoding: utf-8 -*-", - "'''", - "@File : $TM_FILENAME", - "@Time : $CURRENT_YEAR/$CURRENT_MONTH/$CURRENT_DATE $CURRENT_HOUR:$CURRENT_MINUTE:$CURRENT_SECOND", - "@Author : Yan Wen ", - "@Version : 1.0", - "@Contact : z19040042@s.upc.edu.cn", - "@Desc : None", - - "'''", - "", - "# here put the import lib", - "$1" - ], - } -} -``` -# People in relative projects -* [Yang Guan](https://idthanm.github.io/) -# Details about RL -* 强化学习中的CNN一般没有池化层,池化层会让你获得平移不变性,即网络对图像中对象的位置变得不敏感。这对于 ImageNet 这样的分类任务来说是有意义的,但游戏中位置对潜在的奖励至关重要,我们不希望丢失这些信息。 -* 经验回放的动机是:①深度神经网络作为有监督学习模型,要求数据满足独立同分布;②通过强化学习采集的数据之间存在着关联性,利用这些数据进行顺序训练,神经网络表现不稳定,而经验回放可以打破数据间的关联。 \ No newline at end of file diff --git a/docs/Reinforcement Learning.md b/docs/Reinforcement Learning.md index cc15c35..c836e33 100644 --- a/docs/Reinforcement Learning.md +++ b/docs/Reinforcement Learning.md @@ -1,7 +1,11 @@ # Tutorials * [rainbow-is-all-you-need](https://github.com/Curt-Park/rainbow-is-all-you-need) * [MrSyee/pg-is-all-you-need](https://github.com/MrSyee/pg-is-all-you-need) - +* [The Logit and Sigmoid Functions](https://nathanbrixius.wordpress.com/2016/06/04/functions-i-have-known-logit-and-sigmoid/) +* [Generalized Advantage Estimator](https://zhuanlan.zhihu.com/p/139097326) +* [Python浮点算术:争议和限制](https://docs.python.org/zh-cn/3/tutorial/floatingpoint.html) +* [rainbow-is-all-you-need](https://github.com/Curt-Park/rainbow-is-all-you-need) +* [pg-is-all-you-need](https://github.com/MrSyee/pg-is-all-you-need) # Blogs ## [刘建平Pinard](https://www.cnblogs.com/pinard/) * [强化学习(十二) Dueling DQN](https://www.cnblogs.com/pinard/p/9923859.html) @@ -15,8 +19,46 @@ * [Learning Breakout From RAM – Part 2](https://www.codeproject.com/Articles/5271950/Learning-Breakout-From-RAM-Part-2) * [Learning Breakout: Advanced Topics](https://www.codeproject.com/Articles/5271951/Learning-Breakout-Advanced-Topics) +## [born-in-freedom](https://blog.csdn.net/bornfree5511) +1. [Ubuntu助手 — 一键自动安装软件,一键进行系统配置](https://www.guyuehome.com/15107) +2. [深度强化学习专栏 —— 1.研究现状](https://www.guyuehome.com/21403) +3. [深度强化学习专栏 —— 2.手撕DQN算法实现CartPole控制](https://www.guyuehome.com/22329) +4. [深度强化学习专栏 —— 3.实现一阶倒立摆](https://www.guyuehome.com/23439) +5. [深度强化学习专栏 —— 4. 使用ray做分布式计算](https://www.guyuehome.com/?p=26243) +6. [深度强化学习专栏 —— 5. 使用ray的tune组件优化强化学习算法的超参数](https://www.guyuehome.com/?p=26247) +7. [深度强化学习专栏 —— 6. 使用RLLib和ray进行强化学习训练](https://www.guyuehome.com/26251) +8. [深度强化学习专栏 —— 7. 实现机械臂reach某点之PPO算法实现(一)](https://www.guyuehome.com/33650) +9. [深度强化学习专栏 —— 8. 实现机械臂reach某点之PPO算法实现(二)](https://www.guyuehome.com/33663) +10. [深度强化学习专栏 —— 9. 实现机械臂reach某点之PPO算法实现(三)](https://www.guyuehome.com/33682) +11. [深度强化学习专栏 —— 10. 实现机械臂reach某点之环境实现实现](https://www.guyuehome.com/33691) + +## Others +* [spinningup docs](https://spinningup.openai.com/en/latest/user/installation.html) +* [Proximal Policy Optimization Tutorial (Part 1/2: Actor-Critic Method)](https://towardsdatascience.com/proximal-policy-optimization-tutorial-part-1-actor-critic-method-d53f9afffbf6)(do not carefully read now.) +* [some ray/rllib and other rl problems' blogs](https://www.datahubbs.com/) +* [Action Masking with RLlib](https://towardsdatascience.com/action-masking-with-rllib-5e4bec5e7505) +* [This AI designs beautiful Forest Landscapes for Games!](https://medium.com/deepgamingai/this-ai-designs-beautiful-forest-landscapes-for-games-8675e053636e) +* [Chintan Trivedi's homepage](https://medium.com/@chintan.t93), he writes many blogs about AI and games. It's very recommended. +* [Proximal Policy Optimization Tutorial (Part 1/2: Actor-Critic Method)](https://twitter.com/ericwen5986/status/1374361315100172289) +* [Proximal Policy Optimization Tutorial (Part 2/2: GAE and PPO loss)](https://twitter.com/ericwen5986/status/1374361470859767809) +* [Antonin Raffin](https://araffin.github.io/), he is the member of stable baseline3 project. +* [spinningup using in pybullet envs](https://www.etedal.net/2020/04/pybullet-panda_3.html), this is a blog about how to use spinningup to pybullet envs and use the image as the observation. +* [Understanding LSTM Networks](https://colah.github.io/posts/2015-08-Understanding-LSTMs/), this is a good blog introducing lstm. + + # Source codes * [kaixindelele/DRLib](https://github.com/kaixindelele/DRLib), this is a rl lib written by a Chinese, if you want to write your own algorithms library, it maybe useful. +* [robotics-rl-srl](https://github.com/araffin/robotics-rl-srl), S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics. In this project, there are CNN policy and instructions how to connect a real robot using deep rl. +* [zenetio/DeepRL-Robotic](https://github.com/zenetio/DeepRL-Robotic), a deep rl project using gazebo. +* [robotology-playground/pybullet-robot-envs](https://github.com/robotology-playground/pybullet-robot-envs), a deep rl project using pybullet, it is built by a company, there are a lot can study from their codes. But their envs do not introduce images. +* [mahyaret/kuka_rl](https://github.com/mahyaret/kuka_rl), a tutorial tells you how to implement DQN and ppo algorithms to kuka robot grasping. +* [AutodeskRoboticsLab/RLRoboticAssembly](https://github.com/AutodeskRoboticsLab/RLRoboticAssembly), a deep rl robot assembly project build by autodesk, it uses rllib and ray. +* [MorvanZhou/train-robot-arm-from-scratch](https://github.com/MorvanZhou/train-robot-arm-from-scratch), a deep rl robot project build by Morvan. +* **[BarisYazici/deep-rl-grasping](https://github.com/BarisYazici/deep-rl-grasping), a deep rl robot grasping project built by a student in Technical University of Munich. He also released his degree's paper, we can learn a lot from his paper.** +* [mahyaret/gym-panda](https://github.com/mahyaret/gym-panda), this is a pybullet panda environment for deep rl. In the codes, author makes the image as the observation. +* [gaoxiaos/Supermariobros-PPO-pytorch](https://github.com/gaoxiaos/Supermariobros-PPO-pytorch), a tutorial about how to implement deep rl to super mario game, the algorithms are modified from spiningup, and the observation is image. So the code is very suitable for image based deep rl. + +* [ShangtongZhang/reinforcement-learning-an-introduction](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction), this is the python version code of the book reinforcement learning an introduction second edition, the full book and other resources can be found here [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book-2nd.html). # Papers @@ -28,4 +70,8 @@ * [Hindsight Experience Replay](http://arxiv.org/abs/1707.01495) * [Prioritized Experience Replay](http://arxiv.org/abs/1511.05952) * [Proximal Policy Optimization Algorithms](http://arxiv.org/abs/1707.06347) -* [Overcoming Exploration in Reinforcement Learning with Demonstrations](http://arxiv.org/abs/1709.10089) \ No newline at end of file +* [Overcoming Exploration in Reinforcement Learning with Demonstrations](http://arxiv.org/abs/1709.10089) + +# People in relative projects +* [Yang Guan](https://idthanm.github.io/) + diff --git a/docs/deep_learning.md b/docs/deep_learning.md new file mode 100644 index 0000000..f35ffb5 --- /dev/null +++ b/docs/deep_learning.md @@ -0,0 +1,4 @@ +# Deep learning in RL + +* 强化学习中的CNN一般没有池化层,池化层会让你获得平移不变性,即网络对图像中对象的位置变得不敏感。这对于 ImageNet 这样的分类任务来说是有意义的,但游戏中位置对潜在的奖励至关重要,我们不希望丢失这些信息。 +* 经验回放的动机是:①深度神经网络作为有监督学习模型,要求数据满足独立同分布;②通过强化学习采集的数据之间存在着关联性,利用这些数据进行顺序训练,神经网络表现不稳定,而经验回放可以打破数据间的关联。 \ No newline at end of file diff --git a/docs/pybullet.md b/docs/pybullet.md new file mode 100644 index 0000000..2cdb9db --- /dev/null +++ b/docs/pybullet.md @@ -0,0 +1,8 @@ +1. [pybullet杂谈 :使用深度学习拟合相机坐标系与世界坐标系坐标变换关系(一)](https://www.guyuehome.com/24528) +2. [pybullet杂谈 :使用深度学习拟合相机坐标系与世界坐标系坐标变换关系(二)](https://www.guyuehome.com/26255) +3. [pybullet电机控制总结](https://blog.csdn.net/bornfree5511/article/details/108188632) +4. [Part 1 - 自定义gym环境](https://blog.csdn.net/bornfree5511/article/details/108212687) +5. [Part 1.1 - 注册自定义Gym环境](https://blog.csdn.net/bornfree5511/article/details/108212963) +6. [Part 1.2 - 实现一个井字棋游戏的gym环境](https://blog.csdn.net/bornfree5511/article/details/108214740) +7. [Part 1.3 - 熟悉PyBullet](https://blog.csdn.net/bornfree5511/article/details/108307638) +8. [Part 1.4 - 为PyBullet创建Gym环境](https://blog.csdn.net/bornfree5511/article/details/108326084) diff --git a/docs/python.md b/docs/python.md new file mode 100644 index 0000000..f4cc390 --- /dev/null +++ b/docs/python.md @@ -0,0 +1,24 @@ +# Python Knowledge +* [Python中的作用域、global与nonlocal](https://note.qidong.name/2017/07/python-legb/) +* [Delgan/loguru](https://github.com/Delgan/loguru), this is a great python log module, it is much greater than python built in logging module. +* [wandb](https://wandb.ai/site),Developer tools for machine learning. Build better models faster with experiment tracking, dataset . +* logging usage +```python +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', + filename='./logs/client1-{}.log'.format(time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())), + filemode='w') +logger = logging.getLogger(__name__) + +formatter = logging.Formatter('%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s') +stream_handler = logging.StreamHandler() + +stream_handler.setLevel(logging.INFO) +stream_handler.setFormatter(formatter) +logger.addHandler(stream_handler) + +# in the codes. +# logger.info() +# logger.debug() +``` diff --git a/docs/robotics.md b/docs/robotics.md new file mode 100644 index 0000000..7669601 --- /dev/null +++ b/docs/robotics.md @@ -0,0 +1,18 @@ +# Robotics knowledge +* [回到基础——理解几何旋转与欧拉角](https://robodk.com/cn/blog/%E5%87%A0%E4%BD%95%E6%97%8B%E8%BD%AC%E4%B8%8E%E6%AC%A7%E6%8B%89%E8%A7%92/) + +# Some resources about how to implement the RL to real robots +## Source codes +* [kindredresearch/SenseAct](https://github.com/kindredresearch/SenseAct) + +## Papers +* [Setting up a Reinforcement Learning Task with a Real-World Robot](https://arxiv.org/pdf/1803.07067.pdf) +* [Real-World Human-Robot Collaborative Reinforcement Learning](https://arxiv.org/pdf/2003.01156.pdf) + +## Blogs +* [入门机器人强化学习的一些坑:模拟环境篇](https://zhuanlan.zhihu.com/p/300541709) + +## Some comments from Facebook groups + 0|1|2|3 +--|--|--|-- +![](https://github.com/borninfreedom/kuka-reach-drl/blob/main/pictures/rosrl0.jpg)|![](https://github.com/borninfreedom/kuka-reach-drl/blob/main/pictures/rosrl1.jpg)|![](https://github.com/borninfreedom/kuka-reach-drl/blob/main/pictures/rosrl2.jpg)|![](https://github.com/borninfreedom/kuka-reach-drl/blob/main/pictures/rosrl3.jpg) \ No newline at end of file diff --git a/docs/vscode.md b/docs/vscode.md new file mode 100644 index 0000000..0f80042 --- /dev/null +++ b/docs/vscode.md @@ -0,0 +1,56 @@ +# VSCode tricks +## [About python extensions](https://zhuanlan.zhihu.com/p/361654489?utm_source=com.miui.notes&utm_medium=social&utm_oi=903420714332332032) + +## Resolve a.py in A folder import b.py in B folder +* Add the codes below at the top of a .py file +```python +import os,inspect +current_dir=os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) +os.chdir(current_dir) +import sys +sys.path.append('../') +``` +## Add header template in .py files +* Select FIle -> Preference -> User Snippets -> 选择python文件 +* Add the codes below +```python + +{ + // Place your snippets for python here. Each snippet is defined under a snippet name and has a prefix, body and + // description. The prefix is what is used to trigger the snippet and the body will be expanded and inserted. Possible variables are: + // $1, $2 for tab stops, $0 for the final cursor position, and ${1:label}, ${2:another} for placeholders. Placeholders with the + // same ids are connected. + // Example: + // "Print to console": { + // "prefix": "log", + // "body": [ + // "console.log('$1');", + // "$2" + // ], + // "description": "Log output to console" + // } + + + + "HEADER":{ + "prefix": "header", + "body": [ + "#!/usr/bin/env python3", + "# -*- encoding: utf-8 -*-", + "'''", + "@File : $TM_FILENAME", + "@Time : $CURRENT_YEAR/$CURRENT_MONTH/$CURRENT_DATE $CURRENT_HOUR:$CURRENT_MINUTE:$CURRENT_SECOND", + "@Author : Yan Wen ", + "@Version : 1.0", + "@Contact : z19040042@s.upc.edu.cn", + "@Desc : None", + + "'''", + "", + "# here put the import lib", + "$1" + ], + } +} +``` + From b61e772ec425d519a6db7615d590cc59f4dc13d8 Mon Sep 17 00:00:00 2001 From: borninfreedom Date: Wed, 28 Apr 2021 23:05:26 +0800 Subject: [PATCH 4/8] make readme more complete --- README.md | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 37ca0b0..f594877 100644 --- a/README.md +++ b/README.md @@ -26,9 +26,12 @@ pip install pybullet # Wiki -## [Reinforcement Learning]() -## [pybullet]() - +## [Reinforcement Learning](https://github.com/borninfreedom/kuka-reach-drl/blob/rllib/docs/Reinforcement%20Learning.md) +## [pybullet](https://github.com/borninfreedom/kuka-reach-drl/blob/rllib/docs/pybullet.md) +## [robotics](https://github.com/borninfreedom/kuka-reach-drl/blob/rllib/docs/robotics.md) +## [deep learning](https://github.com/borninfreedom/kuka-reach-drl/blob/rllib/docs/deep_learning.md) +## [python](https://github.com/borninfreedom/kuka-reach-drl/blob/rllib/docs/python.md) +## [vscode](https://github.com/borninfreedom/kuka-reach-drl/blob/rllib/docs/vscode.md) From a6d5e6b5418a43c0a7159f8aba827760b5f1f530 Mon Sep 17 00:00:00 2001 From: borninfreedom Date: Thu, 29 Apr 2021 09:42:47 +0800 Subject: [PATCH 5/8] change ppo to impala in the rllib with atari.py --- test/rllib_with_atari.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/rllib_with_atari.py b/test/rllib_with_atari.py index 690eab2..30ce317 100644 --- a/test/rllib_with_atari.py +++ b/test/rllib_with_atari.py @@ -11,14 +11,14 @@ 'framework':'torch', 'render_env':False, 'num_gpus_per_worker':0, - 'num_envs_per_worker':2, + 'num_envs_per_worker':1, } stop = { 'episode_reward_mean': 500 } st=time.time() results = tune.run( - 'PPO', # Specify the algorithm to train + 'IMPALA', # Specify the algorithm to train config=config, stop=stop ) From a8311e7e5d2030ccb0b0ef776d5da42baf131609 Mon Sep 17 00:00:00 2001 From: borninfreedom Date: Thu, 29 Apr 2021 17:26:58 +0800 Subject: [PATCH 6/8] modify reach env --- env/kuka_reach_env.py | 52 +++++++++++++++++-------------------------- 1 file changed, 21 insertions(+), 31 deletions(-) diff --git a/env/kuka_reach_env.py b/env/kuka_reach_env.py index 3292905..8b4c125 100644 --- a/env/kuka_reach_env.py +++ b/env/kuka_reach_env.py @@ -6,7 +6,6 @@ @Author : Yan Wen @Version : 1.0 @Contact : z19040042@s.upc.edu.cn -@License : (C)Copyright 2021-2022, Liugroup-NLPR-CASIA @Desc : None ''' @@ -26,42 +25,33 @@ from numpy import arange import logging import math -from termcolor import colored - -#### 一些变量 ###### -LOGGING_LEVEL = logging.INFO -# is_render=False -# is_good_view=False #这个的作用是在step时加上time.sleep(),把机械比的动作放慢,看的更清,但是会降低训练速度 -######################### - -# logging.basicConfig( -# level=LOGGING_LEVEL, -# format='%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', -# filename='../logs/reach_env.log'.format(time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())), -# filemode='w') -# logger = logging.getLogger(__name__) -# env_logger=logging.getLogger('env.py') - -# logging模块的使用 -# 级别 何时使用 -# DEBUG 细节信息,仅当诊断问题时适用。 -# INFO 确认程序按预期运行 -# WARNING 表明有已经或即将发生的意外(例如:磁盘空间不足)。程序仍按预期进行 -# ERROR 由于严重的问题,程序的某些功能已经不能正常执行 -# CRITICAL 严重的错误,表明程序已不能继续执行 +import ray +from ray import tune +from ray.tune import grid_search +from ray.rllib.env.env_context import EnvContext +from ray.rllib.models import ModelCatalog +from ray.rllib.models.tf.tf_modelv2 import TFModelV2 +from ray.rllib.models.tf.fcnet import FullyConnectedNetwork +from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 +from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC +from ray.rllib.utils.framework import try_import_tf, try_import_torch +from ray.rllib.utils.test_utils import check_learning_achieved + +tf1, tf, tfv = try_import_tf() +torch, nn = try_import_torch() class KukaReachEnv(gym.Env): metadata = { 'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 50 } - max_steps_one_episode = 1000 - def __init__(self, is_render=False, is_good_view=False): + def __init__(self, config:EnvContext): - self.is_render = is_render - self.is_good_view = is_good_view + self.max_steps_one_episode=if config["max_steps_one_episode"] else 1000 + self.is_render = if config["is_render"] else False + self.is_good_view = if config["is_good_view"] else False if self.is_render: p.connect(p.GUI) @@ -123,12 +113,11 @@ def __init__(self, is_render=False, is_good_view=False): self.orientation = p.getQuaternionFromEuler( [0., -math.pi, math.pi / 2.]) - self.seed() + self.seed(config.worker_index*config.num_workers) self.reset() def seed(self, seed=None): - self.np_random, seed = seeding.np_random(seed) - return [seed] + random.seed(seed) def reset(self): #p.connect(p.GUI) @@ -198,6 +187,7 @@ def reset(self): #logging.debug("init_pos={}\n".format(p.getLinkState(self.kuka_id,self.num_joints-1))) p.stepSimulation() self.object_pos = p.getBasePositionAndOrientation(self.object_id)[0] + self.robot_pos=p.getLinkState(self.kuka_id,self.num_joints-1) return np.array(self.object_pos).astype(np.float32) #return np.array(self.robot_pos_obs).astype(np.float32) From 021dcf75c55ca260b49202a343765be0779035d0 Mon Sep 17 00:00:00 2001 From: borninfreedom Date: Fri, 30 Apr 2021 09:30:06 +0800 Subject: [PATCH 7/8] delete pretrain of rllib --- custom_envs/__init__.py | 0 custom_envs/kuka_cam_gripper_reach_env.py | 439 ++++++++++++++++ custom_envs/kuka_cam_reach.py | 580 ++++++++++++++++++++++ custom_envs/kuka_gripper_reach_env.py | 353 +++++++++++++ custom_envs/kuka_reach_env.py | 324 ++++++++++++ pretrained/grasp/model.pt | Bin 76680 -> 0 bytes pretrained/reach-cnn/model.pt | Bin 4969249 -> 0 bytes pretrained/reach-mlp/model.pt | Bin 40839 -> 0 bytes test/test_or.py | 10 + train/reach/impala_with_value.py | 53 ++ 10 files changed, 1759 insertions(+) create mode 100644 custom_envs/__init__.py create mode 100644 custom_envs/kuka_cam_gripper_reach_env.py create mode 100644 custom_envs/kuka_cam_reach.py create mode 100644 custom_envs/kuka_gripper_reach_env.py create mode 100644 custom_envs/kuka_reach_env.py delete mode 100644 pretrained/grasp/model.pt delete mode 100644 pretrained/reach-cnn/model.pt delete mode 100644 pretrained/reach-mlp/model.pt create mode 100644 test/test_or.py create mode 100644 train/reach/impala_with_value.py diff --git a/custom_envs/__init__.py b/custom_envs/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/custom_envs/kuka_cam_gripper_reach_env.py b/custom_envs/kuka_cam_gripper_reach_env.py new file mode 100644 index 0000000..7a9c489 --- /dev/null +++ b/custom_envs/kuka_cam_gripper_reach_env.py @@ -0,0 +1,439 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +''' +@File : kuka_base_env.py +@Time : 2021/04/19 21:42:15 +@Author : Yan Wen +@Version : 1.0 +@Contact : z19040042@s.upc.edu.cn +@Desc : None +''' + +# here put the import lib +import logging +import pybullet as p +import pybullet_data +import os +import gym +from gym import spaces +from gym.utils import seeding +import numpy as np +from math import sqrt +import random +import time +import math +from colorama import Fore, Back, init +import cv2 +init(autoreset=True) +import sys + +# logging.basicConfig( +# level=logging.INFO, +# format='%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', +# #filename='./logs/client1-{}.log'.format(time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())), +# #filemode='w') +# ) + +logger = logging.getLogger(__name__) + +formatter = logging.Formatter( + '%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s' +) + +stream_handler = logging.StreamHandler() + +stream_handler.setLevel(logging.INFO) +stream_handler.setFormatter(formatter) +logger.addHandler(stream_handler) + + +class KukaCamGripperReachEnv(gym.Env): + metadata = { + 'render.modes': ['human', 'rgb_array'], + 'video.frames_per_second': 50 + } + max_steps_one_episode = 200 + + def __init__(self, is_render=False, is_good_view=False): + + self.is_render = is_render + self.is_good_view = is_good_view + + if self.is_render: + p.connect(p.GUI) + else: + p.connect(p.DIRECT) + + self.gripper_length = 0.257 + + self.x_low_obs = 0.2 + self.x_high_obs = 0.7 + self.y_low_obs = -0.3 + self.y_high_obs = 0.3 + self.z_low_obs = 0 + self.z_high_obs = 0.55 + + self.x_low_action = -0.4 + self.x_high_action = 0.4 + self.y_low_action = -0.4 + self.y_high_action = 0.4 + self.z_low_action = -0.6 + self.z_high_action = 0.3 + + self.camera_parameters = { + 'width': 960., + 'height': 720, + 'fov': 60, + 'near': 0.1, + 'far': 100., + 'eye_position': [0.59, 0, 0.8], + 'target_position': [0.55, 0, 0.05], + 'camera_up_vector': + [1, 0, 0], # I really do not know the parameter's effect. + 'light_direction': [ + 0.5, 0, 1 + ], #the direction is from the light source position to the origin of the world frame. + } + + self.view_matrix = p.computeViewMatrixFromYawPitchRoll( + cameraTargetPosition=[0.55, 0, 0.05], + distance=.7, + yaw=90, + pitch=-70, + roll=0, + upAxisIndex=2) + + self.projection_matrix = p.computeProjectionMatrixFOV( + fov=self.camera_parameters['fov'], + aspect=self.camera_parameters['width'] / + self.camera_parameters['height'], + nearVal=self.camera_parameters['near'], + farVal=self.camera_parameters['far']) + + # Change the light position to avoid the shadow affects the picture to the model + p.configureDebugVisualizer(lightPosition=[5, 0, 5]) + + p.resetDebugVisualizerCamera(cameraDistance=1.5, + cameraYaw=0, + cameraPitch=-40, + cameraTargetPosition=[0.55, -0.35, 0.2]) + + self.action_space = spaces.Box(low=np.array( + [self.x_low_action, self.y_low_action, self.z_low_action]), + high=np.array([ + self.x_high_action, + self.y_high_action, + self.z_high_action + ]), + dtype=np.float32) + + self.observation_space = spaces.Box( + low=np.array([ + self.x_low_obs, self.y_low_obs, self.z_low_obs, self.x_low_obs, + self.y_low_obs, self.z_low_obs + self.gripper_length + ]), + high=np.array([ + self.x_high_obs, self.y_high_obs, self.z_high_obs, + self.x_high_obs, self.y_high_obs, + self.z_high_obs + self.gripper_length + ]), + dtype=np.float32) + + self.step_counter = 0 + + self.end_effector_index = 6 + self.gripper_index = 7 + + self.urdf_root_path = pybullet_data.getDataPath() + # lower limits for null space + self.lower_limits = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05] + # upper limits for null space + self.upper_limits = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05] + # joint ranges for null space + self.joint_ranges = [5.8, 4, 5.8, 4, 5.8, 4, 6] + # restposes for null space + self.rest_poses = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0] + # joint damping coefficents + self.joint_damping = [ + 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, + 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001 + ] + + self.init_joint_positions = [ + 0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, + -0.006539, 0.000048, -0.299912, 0.000000, -0.000043, 0.299960, + 0.000000, -0.000200 + ] + + self.orientation = p.getQuaternionFromEuler([math.pi, 0, math.pi]) + + self.seed() + self.reset() + + def seed(self, seed=None): + self.np_random, seed = seeding.np_random(seed) + return [seed] + + def reset(self): + #p.connect(p.GUI) + self.step_counter = 0 + + p.resetSimulation() + #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) + self.terminated = False + p.setGravity(0, 0, -10) + + #这些是周围那些白线,用来观察是否超过了obs的边界 + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, 0], + lineToXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_high_obs, 0], + lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_low_obs, 0], + lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_high_obs, 0], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + + p.loadURDF(os.path.join(self.urdf_root_path, "plane.urdf"), + basePosition=[0, 0, -0.65]) + + self.kuka_id = p.loadSDF( + os.path.join(self.urdf_root_path, + "kuka_iiwa/kuka_with_gripper2.sdf"))[0] + # The return of loadSDF is a tuple (0,) + + table_uid = p.loadURDF(os.path.join(self.urdf_root_path, + "table/table.urdf"), + basePosition=[0.5, 0, -0.65]) + p.changeVisualShape(table_uid, -1, rgbaColor=[1, 1, 1, 1]) + + self.object_id = p.loadURDF( + os.path.join(self.urdf_root_path, "random_urdfs/000/000.urdf"), + basePosition=[ + random.uniform(self.x_low_obs, self.x_high_obs), + random.uniform(self.y_low_obs, self.y_high_obs), 0.01 + ], + baseOrientation=p.getQuaternionFromEuler([0, 0, 0])) + + for i in range(p.getNumJoints(self.kuka_id)): + p.resetJointState( + bodyUniqueId=self.kuka_id, + jointIndex=i, + targetValue=self.init_joint_positions[i], + ) + + p.resetJointState(self.kuka_id, 8, -0.3) + p.resetJointState(self.kuka_id, 10, 0) + p.resetJointState(self.kuka_id, 11, 0.3) + p.resetJointState(self.kuka_id, 13, 0) + + p.stepSimulation() + return self._resolve_obs_return() + + def _resolve_obs_return(self): + """Convert the RGB pic to gray pic and add a channel 1 + + Args: + image ([type]): [description] + """ + (_, _, px, _, + _) = p.getCameraImage(width=960, + height=960, + viewMatrix=self.view_matrix, + projectionMatrix=self.projection_matrix, + renderer=p.ER_BULLET_HARDWARE_OPENGL) + self.images = px + self.images = self.images[:, :, : + 3] # the 4th channel is alpha channel, we do not need it. + + if self.images is not None: + image = cv2.cvtColor(self.images, cv2.COLOR_RGB2GRAY) + image = cv2.resize(image, (84, 84))[None, :, :] / 255. + return image + else: + return np.zeros((1, 84, 84)) + + + def step(self, action): + dv = 0.005 + dx = action[0] * dv + dy = action[1] * dv + dz = action[2] * dv + + current_pos = np.array( + p.getLinkState(self.kuka_id, + self.end_effector_index)[4]).astype(np.float32) + + new_robot_pos = [ + current_pos[0] + dx, current_pos[1] + dy, current_pos[2] + dz + ] + + robot_joint_positions = p.calculateInverseKinematics( + bodyUniqueId=self.kuka_id, + endEffectorLinkIndex=self.end_effector_index, + targetPosition=[ + new_robot_pos[0], new_robot_pos[1], new_robot_pos[2] + ], + targetOrientation=self.orientation, + jointDamping=self.joint_damping, + ) + + for i in range(self.end_effector_index): + p.resetJointState( + bodyUniqueId=self.kuka_id, + jointIndex=i, + targetValue=robot_joint_positions[i], + ) + p.stepSimulation() + + if self.is_good_view: + time.sleep(0.05) + + self.step_counter += 1 + return self._reward() + + def _reward(self): + + robot_end_effector_position = np.array( + p.getLinkState(self.kuka_id, + self.end_effector_index)[4]).astype(np.float32) + + # We got the position of the robot end effector not gripper end effector, + # so we need to compasent the gripper length + # What we want is the gripper end effector position + robot_end_effector_position[2] -= self.gripper_length + object_position = np.array( + p.getBasePositionAndOrientation(self.object_id)[0]).astype( + np.float32) + + square_dx = np.float32( + (robot_end_effector_position[0] - object_position[0])**2) + square_dy = np.float32( + (robot_end_effector_position[1] - object_position[1])**2) + square_dz = np.float32( + (robot_end_effector_position[2] - object_position[2])**2) + + distance = np.float32(sqrt(square_dx + square_dy + square_dz)) + + x = np.float32(robot_end_effector_position[0]) + y = np.float32(robot_end_effector_position[1]) + z = np.float32(robot_end_effector_position[2]) + logger.debug(Fore.RED + 'x,y,z={},{},{}'.format(x, y, z)) + + terminated = bool(x < self.x_low_obs or x > self.x_high_obs + or y < self.y_low_obs or y > self.y_high_obs + or z < self.z_low_obs or z > self.z_high_obs) + + if terminated: + reward = -0.1 + self.terminated = True + + elif self.step_counter > self.max_steps_one_episode: + reward = -0.1 + self.terminated = True + + elif distance < 0.1: + reward = 1 + self.terminated = True + else: + reward = 0 + self.terminated = False + + info = {'distance:', distance} + + return self._resolve_obs_return(), reward, self.terminated, info + + def close(self): + p.disconnect() + + +class CustomSkipFrame(gym.Wrapper): + """ Make a 4 frame skip, so the observation space will change to (4,84,84) from (1,84,84) + + Args: + gym ([type]): [description] + """ + def __init__(self, env, skip=4): + super(CustomSkipFrame, self).__init__(env) + self.observation_space = spaces.Box(low=0, + high=1, + shape=(skip, 84, 84)) + self.skip = skip + + def step(self, action): + total_reward = 0 + states = [] + state, reward, done, info = self.env.step(action) + for i in range(self.skip): + if not done: + state, reward, done, info = self.env.step(action) + total_reward += reward + states.append(state) + else: + states.append(state) + states = np.concatenate(states, 0)[None, :, :, :] + return states.astype(np.float32), reward, done, info + + def reset(self): + state = self.env.reset() + states = np.concatenate([state for _ in range(self.skip)], + 0)[None, :, :, :] + return states.astype(np.float32) + + +if __name__ == '__main__': + import matplotlib.pyplot as plt + # 这一部分是做baseline,即让机械臂随机选择动作,看看能够得到的分数 + env = KukaCamGripperReachEnv(is_good_view=True,is_render=True) + env=CustomSkipFrame(env) + + print('env={}'.format(env)) + print(env.observation_space.shape) + # print(env.observation_space.sample()) + # print(env.action_space.sample()) + print(env.action_space.shape) + obs = env.reset() + print(Fore.RED + 'obs={}'.format(obs)) + action = env.action_space.sample() + obs, reward, done, info = env.step(action) + print('obs={},reward={},done={},info={}'.format(obs, reward, done, info)) + + + sum_reward = 0 + success_times = 0 + for i in range(100): + env.reset() + for i in range(1000): + action = env.action_space.sample() + obs, reward, done, info = env.step(action) + img = obs[0][0] + plt.imshow(img, cmap='gray') + plt.show() + + print('obs={},reward={},done={},info={}'.format( + obs, reward, done, info)) + sum_reward += reward + if reward == 1: + success_times += 1 + if done: + break + # time.sleep(0.1) + print() + print('sum_reward={}'.format(sum_reward)) + print('success rate={}'.format(success_times / 50)) diff --git a/custom_envs/kuka_cam_reach.py b/custom_envs/kuka_cam_reach.py new file mode 100644 index 0000000..228dc81 --- /dev/null +++ b/custom_envs/kuka_cam_reach.py @@ -0,0 +1,580 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +''' +@File : kuka_reach_env.py +@Time : 2021/03/20 14:33:24 +@Author : Yan Wen +@Version : 1.0 +@Contact : z19040042@s.upc.edu.cn +@License : (C)Copyright 2021-2022, Liugroup-NLPR-CASIA +@Desc : None +''' + +# here put the import lib + +import pybullet as p +import pybullet_data +import os +import sys +import gym +from gym import spaces +from gym.utils import seeding +import numpy as np +from math import sqrt +import random +import time +from numpy import arange +import logging + +import math +import cv2 +import torch.nn.functional as F +import torchvision.transforms as T +from PIL import Image +from termcolor import colored +import torch +import matplotlib.pyplot as plt +from colorama import Fore, init, Back +import sys +import os + +init(autoreset=True) # this lets colorama takes effect only in current line. +# Otherwise, colorama will let the sentences below 'print(Fore.GREEN+'xx')' +# all become green color. + +#### 一些变量 ###### +#LOGGING_LEVEL=logging.INFO +# is_render=False +# is_good_view=False #这个的作用是在step时加上time.sleep(),把机械比的动作放慢,看的更清,但是会降低训练速度 +######################### + +# logging.basicConfig( +# level=LOGGING_LEVEL, +# format='%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', +# filename='../logs/reach_env.log'.format(time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())), +# filemode='w') +# logger = logging.getLogger(__name__) +# env_logger=logging.getLogger('env.py') + +# logging模块的使用 +# 级别 何时使用 +# DEBUG 细节信息,仅当诊断问题时适用。 +# INFO 确认程序按预期运行 +# WARNING 表明有已经或即将发生的意外(例如:磁盘空间不足)。程序仍按预期进行 +# ERROR 由于严重的问题,程序的某些功能已经不能正常执行 +# CRITICAL 严重的错误,表明程序已不能继续执行 + + +class KukaCamReachEnv(gym.Env): + metadata = { + 'render.modes': ['human', 'rgb_array'], + 'video.frames_per_second': 50 + } + max_steps_one_episode = 2000 + + # final_image_size=40 + # resize = T.Compose([T.ToPILImage(), + # T.Resize(final_image_size, interpolation=Image.CUBIC), + # T.ToTensor()]) + + def __init__(self, is_render=False, is_good_view=False): + + # some camera parameters + # all the parameters are tested with test/slide_bar_for_camera.py file. + + self.camera_parameters = { + 'width': 960., + 'height': 720, + 'fov': 60, + 'near': 0.1, + 'far': 100., + 'eye_position': [0.59, 0, 0.8], + 'target_position': [0.55, 0, 0.05], + 'camera_up_vector': + [1, 0, 0], # I really do not know the parameter's effect. + 'light_direction': [ + 0.5, 0, 1 + ], #the direction is from the light source position to the origin of the world frame. + } + + self.device = torch.device( + "cpu" if torch.cuda.is_available() else "cpu") + + # self.view_matrix=p.computeViewMatrix( + # cameraEyePosition=self.camera_parameters['eye_position'], + # cameraTargetPosition=self.camera_parameters['target_position'], + # cameraUpVector=self.camera_parameters['camera_up_vector'] + # ) + self.view_matrix = p.computeViewMatrixFromYawPitchRoll( + cameraTargetPosition=[0.55, 0, 0.05], + distance=.7, + yaw=90, + pitch=-70, + roll=0, + upAxisIndex=2) + + self.projection_matrix = p.computeProjectionMatrixFOV( + fov=self.camera_parameters['fov'], + aspect=self.camera_parameters['width'] / + self.camera_parameters['height'], + nearVal=self.camera_parameters['near'], + farVal=self.camera_parameters['far']) + + self.is_render = is_render + self.is_good_view = is_good_view + + if self.is_render: + p.connect(p.GUI) + else: + p.connect(p.DIRECT) + + self.x_low_obs = 0.2 + self.x_high_obs = 0.7 + self.y_low_obs = -0.3 + self.y_high_obs = 0.3 + self.z_low_obs = 0 + self.z_high_obs = 0.55 + + self.x_low_action = -0.4 + self.x_high_action = 0.4 + self.y_low_action = -0.4 + self.y_high_action = 0.4 + self.z_low_action = -0.6 + self.z_high_action = 0.3 + + p.configureDebugVisualizer(lightPosition=[5, 0, 5]) + p.resetDebugVisualizerCamera(cameraDistance=1.5, + cameraYaw=0, + cameraPitch=-40, + cameraTargetPosition=[0.55, -0.35, 0.2]) + + self.action_space = spaces.Box(low=np.array( + [self.x_low_action, self.y_low_action, self.z_low_action]), + high=np.array([ + self.x_high_action, + self.y_high_action, + self.z_high_action + ]), + dtype=np.float32) + # self.observation_space=spaces.Box(low=np.array([self.x_low_obs,self.y_low_obs,self.z_low_obs]), + # high=np.array([self.x_high_obs,self.y_high_obs,self.z_high_obs]), + # dtype=np.float32) + + self.observation_space = spaces.Box(low=0, high=1, shape=(1, 84, 84)) + + self.step_counter = 0 + + self.urdf_root_path = pybullet_data.getDataPath() + # lower limits for null space + self.lower_limits = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05] + # upper limits for null space + self.upper_limits = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05] + # joint ranges for null space + self.joint_ranges = [5.8, 4, 5.8, 4, 5.8, 4, 6] + # restposes for null space + self.rest_poses = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0] + # joint damping coefficents + self.joint_damping = [ + 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001 + ] + + self.init_joint_positions = [ + 0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, + -0.006539 + ] + + self.orientation = p.getQuaternionFromEuler( + [0., -math.pi, math.pi / 2.]) + + self.seed() + self.reset() + + def seed(self, seed=None): + self.np_random, seed = seeding.np_random(seed) + return [seed] + + def reset(self): + #p.connect(p.GUI) + self.step_counter = 0 + + p.resetSimulation() + #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) + self.terminated = False + p.setGravity(0, 0, -10) + + #这些是周围那些白线,用来观察是否超过了obs的边界 + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, 0], + lineToXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_high_obs, 0], + lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_low_obs, 0], + lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_high_obs, 0], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + + p.loadURDF(os.path.join(self.urdf_root_path, "plane.urdf"), + basePosition=[0, 0, -0.65]) + self.kuka_id = p.loadURDF(os.path.join(self.urdf_root_path, + "kuka_iiwa/model.urdf"), + useFixedBase=True) + table_uid = p.loadURDF(os.path.join(self.urdf_root_path, + "table/table.urdf"), + basePosition=[0.5, 0, -0.65]) + p.changeVisualShape(table_uid, -1, rgbaColor=[1, 1, 1, 1]) + # p.loadURDF(os.path.join(self.urdf_root_path, "tray/traybox.urdf"),basePosition=[0.55,0,0]) + #object_id=p.loadURDF(os.path.join(self.urdf_root_path, "random_urdfs/000/000.urdf"), basePosition=[0.53,0,0.02]) + self.object_id = p.loadURDF(os.path.join(self.urdf_root_path, + "random_urdfs/000/000.urdf"), + basePosition=[ + random.uniform(self.x_low_obs, + self.x_high_obs), + random.uniform(self.y_low_obs, + self.y_high_obs), 0.01 + ]) + + self.num_joints = p.getNumJoints(self.kuka_id) + + for i in range(self.num_joints): + p.resetJointState( + bodyUniqueId=self.kuka_id, + jointIndex=i, + targetValue=self.init_joint_positions[i], + ) + + self.robot_pos_obs = p.getLinkState(self.kuka_id, + self.num_joints - 1)[4] + #logging.debug("init_pos={}\n".format(p.getLinkState(self.kuka_id,self.num_joints-1))) + p.stepSimulation() + + # (_,_,self.images,_,_) = p.getCameraImage( + # width=self.camera_parameters['width'], + # height=self.camera_parameters['height'], + # viewMatrix=self.view_matrix, + # projectionMatrix=self.projection_matrix, + + # renderer=p.ER_BULLET_HARDWARE_OPENGL + # ) + + (_, _, px, _, + _) = p.getCameraImage(width=960, + height=960, + viewMatrix=self.view_matrix, + projectionMatrix=self.projection_matrix, + renderer=p.ER_BULLET_HARDWARE_OPENGL) + self.images = px + + p.enableJointForceTorqueSensor(bodyUniqueId=self.kuka_id, + jointIndex=self.num_joints - 1, + enableSensor=True) + + # print(Fore.GREEN+'force_sensor={}'.format(self._get_force_sensor_value())) + + self.object_pos = p.getBasePositionAndOrientation(self.object_id)[0] + #return np.array(self.object_pos).astype(np.float32) + #return np.array(self.robot_pos_obs).astype(np.float32) + self.images = self.images[:, :, : + 3] # the 4th channel is alpha channel, we do not need it. + #return self._process_image(self.images) + + return self._process_image(self.images) + + def _process_image(self, image): + """Convert the RGB pic to gray pic and add a channel 1 + + Args: + image ([type]): [description] + """ + + if image is not None: + image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) + image = cv2.resize(image, (84, 84))[None, :, :] / 255. + return image + else: + return np.zeros((1, 84, 84)) + + # image=image.transpose((2,0,1)) + # image=np.ascontiguousarray(image,dtype=np.float32)/255. + # image=torch.from_numpy(image) + # #self.processed_image=self.resize(image).unsqueeze(0).to(self.device) + # self.processed_image=self.resize(image).to(self.device) + # return self.processed_image + + + def step(self, action): + dv = 0.005 + dx = action[0] * dv + dy = action[1] * dv + dz = action[2] * dv + + self.current_pos = p.getLinkState(self.kuka_id, self.num_joints - 1)[4] + # logging.debug("self.current_pos={}\n".format(self.current_pos)) + self.new_robot_pos = [ + self.current_pos[0] + dx, self.current_pos[1] + dy, + self.current_pos[2] + dz + ] + #logging.debug("self.new_robot_pos={}\n".format(self.new_robot_pos)) + self.robot_joint_positions = p.calculateInverseKinematics( + bodyUniqueId=self.kuka_id, + endEffectorLinkIndex=self.num_joints - 1, + targetPosition=[ + self.new_robot_pos[0], self.new_robot_pos[1], + self.new_robot_pos[2] + ], + targetOrientation=self.orientation, + jointDamping=self.joint_damping, + ) + for i in range(self.num_joints): + p.resetJointState( + bodyUniqueId=self.kuka_id, + jointIndex=i, + targetValue=self.robot_joint_positions[i], + ) + p.stepSimulation() + + #在代码开始部分,如果定义了is_good_view,那么机械臂的动作会变慢,方便观察 + if self.is_good_view: + time.sleep(0.05) + + self.step_counter += 1 + + return self._reward() + + def _reward(self): + + #一定注意是取第4个值,请参考pybullet手册的这个函数返回值的说明 + self.robot_state = p.getLinkState(self.kuka_id, self.num_joints - 1)[4] + # self.object_state=p.getBasePositionAndOrientation(self.object_id) + # self.object_state=np.array(self.object_state).astype(np.float32) + # + self.object_state = np.array( + p.getBasePositionAndOrientation(self.object_id)[0]).astype( + np.float32) + + square_dx = (self.robot_state[0] - self.object_state[0])**2 + square_dy = (self.robot_state[1] - self.object_state[1])**2 + square_dz = (self.robot_state[2] - self.object_state[2])**2 + + #用机械臂末端和物体的距离作为奖励函数的依据 + self.distance = sqrt(square_dx + square_dy + square_dz) + #print(self.distance) + + x = self.robot_state[0] + y = self.robot_state[1] + z = self.robot_state[2] + + #如果机械比末端超过了obs的空间,也视为done,而且会给予一定的惩罚 + terminated = bool(x < self.x_low_obs or x > self.x_high_obs + or y < self.y_low_obs or y > self.y_high_obs + or z < self.z_low_obs or z > self.z_high_obs) + + if terminated: + reward = -0.1 + self.terminated = True + + #如果机械臂一直无所事事,在最大步数还不能接触到物体,也需要给一定的惩罚 + elif self.step_counter > self.max_steps_one_episode: + reward = -0.1 + self.terminated = True + + elif self.distance < 0.1: + reward = 1 + self.terminated = True + else: + reward = 0 + self.terminated = False + + info = {'distance:', self.distance} + (_, _, px, _, + _) = p.getCameraImage(width=960, + height=960, + viewMatrix=self.view_matrix, + projectionMatrix=self.projection_matrix, + renderer=p.ER_BULLET_HARDWARE_OPENGL) + self.images = px + self.processed_image = self._process_image(self.images) + #self.observation=self.robot_state + self.observation = self.object_state + return self.processed_image, reward, self.terminated, info + + def close(self): + p.disconnect() + + def run_for_debug(self, target_position): + temp_robot_joint_positions = p.calculateInverseKinematics( + bodyUniqueId=self.kuka_id, + endEffectorLinkIndex=self.num_joints - 1, + targetPosition=target_position, + targetOrientation=self.orientation, + jointDamping=self.joint_damping, + ) + for i in range(self.num_joints): + p.resetJointState( + bodyUniqueId=self.kuka_id, + jointIndex=i, + targetValue=temp_robot_joint_positions[i], + ) + p.stepSimulation() + + #在代码开始部分,如果定义了is_good_view,那么机械臂的动作会变慢,方便观察 + if self.is_good_view: + time.sleep(0.05) + + return self._get_force_sensor_value() + + def _get_force_sensor_value(self): + force_sensor_value = p.getJointState(bodyUniqueId=self.kuka_id, + jointIndex=self.num_joints - + 1)[2][2] + # the first 2 stands for jointReactionForces, the second 2 stands for Fz, + # the pybullet methods' return is a tuple,so can not + # index it with str like dict. I think it can be improved + # that return value is a dict rather than tuple. + return force_sensor_value + + +class CustomSkipFrame(gym.Wrapper): + """ Make a 4 frame skip, so the observation space will change to (4,84,84) from (1,84,84) + + Args: + gym ([type]): [description] + """ + def __init__(self, env, skip=4): + super(CustomSkipFrame, self).__init__(env) + self.observation_space = spaces.Box(low=0, + high=1, + shape=(skip, 84, 84)) + self.skip = skip + + def step(self, action): + total_reward = 0 + states = [] + state, reward, done, info = self.env.step(action) + for i in range(self.skip): + if not done: + state, reward, done, info = self.env.step(action) + total_reward += reward + states.append(state) + else: + states.append(state) + states = np.concatenate(states, 0)[None, :, :, :] + return states.astype(np.float32), reward, done, info + + def reset(self): + state = self.env.reset() + states = np.concatenate([state for _ in range(self.skip)], + 0)[None, :, :, :] + return states.astype(np.float32) + + +if __name__ == '__main__': + # 这一部分是做baseline,即让机械臂随机选择动作,看看能够得到的分数 + import matplotlib.pyplot as plt + env = KukaCamReachEnv(is_good_view=True, is_render=False) + env = CustomSkipFrame(env) + + obs = env.reset() + print(obs) + print(obs.shape) + + img = obs[0][0] + plt.imshow(img, cmap='gray') + plt.show() + + # all the below are some debug codes, if you have interests, look through. + + # b=a[:,:,:3] + # c=b.transpose((2,0,1)) + # #c=b + # d=np.ascontiguousarray(c,dtype=np.float32)/255 + # e=torch.from_numpy(d) + # resize=T.Compose([T.ToPILImage(), + # T.Resize(40,interpolation=Image.CUBIC), + # T.ToTensor()]) + + # f=resize(e).unsqueeze(0) + # #print(f) + # # g=f.unsqueeze(0) + # # print(g) + # #f.transpose((2,0,1)) + + # plt.imshow(f.cpu().squeeze(0).permute(1, 2, 0).numpy(), + # interpolation='none') + + # #plt.imshow(f) + # plt.show() + + # resize = T.Compose([T.ToPILImage(), + # T.Resize(40, interpolation=Image.CUBIC), + # T.ToTensor()]) + +# print(env) +# print(env.observation_space.shape) +# print(env.observation_space.sample()) + +# for i in range(10): +# a=env.reset() +# b=a[:,:,:3] +# """ +# matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, +# alpha=None, vmin=None, vmax=None, origin=None, extent=None, *, filternorm=True, +# filterrad=4.0, resample=None, url=None, data=None, **kwargs) + +# Xarray-like or PIL image +# The image data. Supported array shapes are: + +# (M, N): an image with scalar data. The values are mapped to colors using normalization and a colormap. See parameters norm, cmap, vmin, vmax. +# (M, N, 3): an image with RGB values (0-1 float or 0-255 int). +# (M, N, 4): an image with RGBA values (0-1 float or 0-255 int), i.e. including transparency. +# The first two dimensions (M, N) define the rows and columns of the image. +# Out-of-range RGB(A) values are clipped. +# """ +# plt.imshow(b) +# plt.show() +# time.sleep(1) + +# for i in range(720): +# for j in range(720): +# for k in range(3): +# if not a[i][j][k]==b[i][j][k]: +# print(Fore.RED+'there is unequal') +# raise ValueError('there is unequal.') +# print('check complete') + +#print(a) +#force_sensor=env.run_for_debug([0.6,0.0,0.03]) +# print(Fore.RED+'after force sensor={}'.format(force_sensor)) +#print(env.action_space.sample()) + +# sum_reward=0 +# for i in range(10): +# env.reset() +# for i in range(2000): +# action=env.action_space.sample() +# #action=np.array([0,0,0.47-i/1000]) +# obs,reward,done,info=env.step(action) +# # print("i={},\naction={},\nobs={},\ndone={},\n".format(i,action,obs,done,)) +# print(colored("reward={},info={}".format(reward,info),"cyan")) +# # print(colored("info={}".format(info),"cyan")) +# sum_reward+=reward +# if done: +# break +# # time.sleep(0.1) +# print() +# print(sum_reward) diff --git a/custom_envs/kuka_gripper_reach_env.py b/custom_envs/kuka_gripper_reach_env.py new file mode 100644 index 0000000..6ac131a --- /dev/null +++ b/custom_envs/kuka_gripper_reach_env.py @@ -0,0 +1,353 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +''' +@File : kuka_base_env.py +@Time : 2021/04/19 21:42:15 +@Author : Yan Wen +@Version : 1.0 +@Contact : z19040042@s.upc.edu.cn +@Desc : None +''' + +# here put the import lib +import logging +import pybullet as p +import pybullet_data +import os +import gym +from gym import spaces +from gym.utils import seeding +import numpy as np +from math import sqrt +import random +import time +import math +from colorama import Fore, Back, init + +init(autoreset=True) +import sys + +# logging.basicConfig( +# level=logging.INFO, +# format='%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', +# #filename='./logs/client1-{}.log'.format(time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())), +# #filemode='w') +# ) + +logger = logging.getLogger(__name__) + +formatter = logging.Formatter( + '%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s' +) + +stream_handler = logging.StreamHandler() + +stream_handler.setLevel(logging.INFO) +stream_handler.setFormatter(formatter) +logger.addHandler(stream_handler) + + +class KukaGripperReachEnv(gym.Env): + metadata = { + 'render.modes': ['human', 'rgb_array'], + 'video.frames_per_second': 50 + } + max_steps_one_episode = 1000 + + def __init__(self, is_render=False, is_good_view=False): + + self.is_render = is_render + self.is_good_view = is_good_view + + if self.is_render: + p.connect(p.GUI) + else: + p.connect(p.DIRECT) + + self.gripper_length = 0.257 + + self.x_low_obs = 0.2 + self.x_high_obs = 0.7 + self.y_low_obs = -0.3 + self.y_high_obs = 0.3 + self.z_low_obs = 0 + self.z_high_obs = 0.55 + + self.x_low_action = -0.4 + self.x_high_action = 0.4 + self.y_low_action = -0.4 + self.y_high_action = 0.4 + self.z_low_action = -0.6 + self.z_high_action = 0.3 + + p.resetDebugVisualizerCamera(cameraDistance=1.5, + cameraYaw=0, + cameraPitch=-40, + cameraTargetPosition=[0.55, -0.35, 0.2]) + + self.action_space = spaces.Box(low=np.array( + [self.x_low_action, self.y_low_action, self.z_low_action]), + high=np.array([ + self.x_high_action, + self.y_high_action, + self.z_high_action + ]), + dtype=np.float32) + + self.observation_space = spaces.Box( + low=np.array([ + self.x_low_obs, self.y_low_obs, self.z_low_obs, self.x_low_obs, + self.y_low_obs, self.z_low_obs + self.gripper_length + ]), + high=np.array([ + self.x_high_obs, self.y_high_obs, self.z_high_obs, + self.x_high_obs, self.y_high_obs, + self.z_high_obs + self.gripper_length + ]), + dtype=np.float32) + + self.step_counter = 0 + + self.end_effector_index = 6 + self.gripper_index = 7 + + self.urdf_root_path = pybullet_data.getDataPath() + # lower limits for null space + self.lower_limits = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05] + # upper limits for null space + self.upper_limits = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05] + # joint ranges for null space + self.joint_ranges = [5.8, 4, 5.8, 4, 5.8, 4, 6] + # restposes for null space + self.rest_poses = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0] + # joint damping coefficents + self.joint_damping = [ + 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, + 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001 + ] + + self.init_joint_positions = [ + 0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, + -0.006539, 0.000048, -0.299912, 0.000000, -0.000043, 0.299960, + 0.000000, -0.000200 + ] + + self.orientation = p.getQuaternionFromEuler([math.pi, 0, math.pi]) + + self.seed() + self.reset() + + def seed(self, seed=None): + self.np_random, seed = seeding.np_random(seed) + return [seed] + + def reset(self): + #p.connect(p.GUI) + self.step_counter = 0 + + p.resetSimulation() + #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) + self.terminated = False + p.setGravity(0, 0, -10) + + #这些是周围那些白线,用来观察是否超过了obs的边界 + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, 0], + lineToXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_high_obs, 0], + lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_low_obs, 0], + lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_high_obs, 0], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + + p.loadURDF(os.path.join(self.urdf_root_path, "plane.urdf"), + basePosition=[0, 0, -0.65]) + + self.kuka_id = p.loadSDF( + os.path.join(self.urdf_root_path, + "kuka_iiwa/kuka_with_gripper2.sdf"))[0] + # The return of loadSDF is a tuple (0,) + + table_uid = p.loadURDF(os.path.join(self.urdf_root_path, + "table/table.urdf"), + basePosition=[0.5, 0, -0.65]) + p.changeVisualShape(table_uid, -1, rgbaColor=[1, 1, 1, 1]) + + self.object_id = p.loadURDF( + os.path.join(self.urdf_root_path, "random_urdfs/000/000.urdf"), + basePosition=[ + random.uniform(self.x_low_obs, self.x_high_obs), + random.uniform(self.y_low_obs, self.y_high_obs), 0.01 + ], + baseOrientation=p.getQuaternionFromEuler([0, 0, 0])) + + for i in range(p.getNumJoints(self.kuka_id)): + p.resetJointState( + bodyUniqueId=self.kuka_id, + jointIndex=i, + targetValue=self.init_joint_positions[i], + ) + + p.resetJointState(self.kuka_id, 8, -0.3) + p.resetJointState(self.kuka_id, 10, 0) + p.resetJointState(self.kuka_id, 11, 0.3) + p.resetJointState(self.kuka_id, 13, 0) + + p.stepSimulation() + return self._resolve_obs_return() + + def _resolve_obs_return(self): + object_position = list( + p.getBasePositionAndOrientation(self.object_id)[0]) + robot_end_effector_position = list( + p.getLinkState(self.kuka_id, self.end_effector_index)[4]) + + logger.debug(Fore.BLUE + 'object_position={}'.format(object_position)) + logger.debug(Fore.YELLOW + 'robot end effector position={}'.format( + robot_end_effector_position)) + + return np.array(object_position + robot_end_effector_position).astype( + np.float32) + + def step(self, action): + dv = 0.005 + dx = action[0] * dv + dy = action[1] * dv + dz = action[2] * dv + + current_pos = np.array( + p.getLinkState(self.kuka_id, + self.end_effector_index)[4]).astype(np.float32) + + new_robot_pos = [ + current_pos[0] + dx, current_pos[1] + dy, current_pos[2] + dz + ] + + robot_joint_positions = p.calculateInverseKinematics( + bodyUniqueId=self.kuka_id, + endEffectorLinkIndex=self.end_effector_index, + targetPosition=[ + new_robot_pos[0], new_robot_pos[1], new_robot_pos[2] + ], + targetOrientation=self.orientation, + jointDamping=self.joint_damping, + ) + + for i in range(self.end_effector_index): + p.resetJointState( + bodyUniqueId=self.kuka_id, + jointIndex=i, + targetValue=robot_joint_positions[i], + ) + p.stepSimulation() + + if self.is_good_view: + time.sleep(0.05) + + self.step_counter += 1 + return self._reward() + + def _reward(self): + + robot_end_effector_position = np.array( + p.getLinkState(self.kuka_id, + self.end_effector_index)[4]).astype(np.float32) + + # We got the position of the robot end effector not gripper end effector, + # so we need to compasent the gripper length + # What we want is the gripper end effector position + robot_end_effector_position[2] -= self.gripper_length + object_position = np.array( + p.getBasePositionAndOrientation(self.object_id)[0]).astype( + np.float32) + + square_dx = np.float32( + (robot_end_effector_position[0] - object_position[0])**2) + square_dy = np.float32( + (robot_end_effector_position[1] - object_position[1])**2) + square_dz = np.float32( + (robot_end_effector_position[2] - object_position[2])**2) + + distance = np.float32(sqrt(square_dx + square_dy + square_dz)) + + x = np.float32(robot_end_effector_position[0]) + y = np.float32(robot_end_effector_position[1]) + z = np.float32(robot_end_effector_position[2]) + logger.debug(Fore.RED + 'x,y,z={},{},{}'.format(x, y, z)) + + terminated = bool(x < self.x_low_obs or x > self.x_high_obs + or y < self.y_low_obs or y > self.y_high_obs + or z < self.z_low_obs or z > self.z_high_obs) + + if terminated: + reward = -0.1 + self.terminated = True + + elif self.step_counter > self.max_steps_one_episode: + reward = -0.1 + self.terminated = True + + elif distance < 0.1: + reward = 1 + self.terminated = True + else: + reward = 0 + self.terminated = False + + info = {'distance:', distance} + + return self._resolve_obs_return(), reward, self.terminated, info + + def close(self): + p.disconnect() + + +if __name__ == '__main__': + # 这一部分是做baseline,即让机械臂随机选择动作,看看能够得到的分数 + env = KukaGripperReachEnv(is_good_view=True,is_render=True) + print('env={}'.format(env)) + print(env.observation_space.shape) + # print(env.observation_space.sample()) + # print(env.action_space.sample()) + print(env.action_space.shape) + obs = env.reset() + print(Fore.RED + 'obs={}'.format(obs)) + action = env.action_space.sample() + obs, reward, done, info = env.step(action) + print('obs={},reward={},done={},info={}'.format(obs, reward, done, info)) + + sum_reward = 0 + success_times = 0 + for i in range(100): + env.reset() + for i in range(1000): + action = env.action_space.sample() + obs, reward, done, info = env.step(action) + print('obs={},reward={},done={},info={}'.format( + obs, reward, done, info)) + sum_reward += reward + if reward == 1: + success_times += 1 + if done: + break + # time.sleep(0.1) + print() + print('sum_reward={}'.format(sum_reward)) + print('success rate={}'.format(success_times / 50)) diff --git a/custom_envs/kuka_reach_env.py b/custom_envs/kuka_reach_env.py new file mode 100644 index 0000000..5913d42 --- /dev/null +++ b/custom_envs/kuka_reach_env.py @@ -0,0 +1,324 @@ +#!/usr/bin/env python3 +# -*- encoding: utf-8 -*- +''' +@File : kuka_reach_env.py +@Time : 2021/03/20 14:33:24 +@Author : Yan Wen +@Version : 1.0 +@Contact : z19040042@s.upc.edu.cn +@Desc : None +''' + +# here put the import lib + +import pybullet as p +import pybullet_data +import os +import sys +import gym +from gym import spaces +from gym.utils import seeding +import numpy as np +from math import sqrt +import random +import time +from numpy import arange +import logging +import math + +import ray +from ray import tune +from ray.tune import grid_search +from ray.rllib.env.env_context import EnvContext +from ray.rllib.models import ModelCatalog +from ray.rllib.models.tf.tf_modelv2 import TFModelV2 +from ray.rllib.models.tf.fcnet import FullyConnectedNetwork +from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 +from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC +from ray.rllib.utils.framework import try_import_tf, try_import_torch +from ray.rllib.utils.test_utils import check_learning_achieved + +tf1, tf, tfv = try_import_tf() +torch, nn = try_import_torch() + +class KukaReachEnv(gym.Env): + metadata = { + 'render.modes': ['human', 'rgb_array'], + 'video.frames_per_second': 50 + } + + def __init__(self, config:EnvContext): + + self.max_steps_one_episode=config["max_steps_one_episode"] + self.is_render = config["is_render"] + self.is_good_view = config["is_good_view"] + + if self.is_render: + p.connect(p.GUI) + else: + p.connect(p.DIRECT) + + self.x_low_obs = 0.2 + self.x_high_obs = 0.7 + self.y_low_obs = -0.3 + self.y_high_obs = 0.3 + self.z_low_obs = 0 + self.z_high_obs = 0.55 + + self.x_low_action = -0.4 + self.x_high_action = 0.4 + self.y_low_action = -0.4 + self.y_high_action = 0.4 + self.z_low_action = -0.6 + self.z_high_action = 0.3 + + p.resetDebugVisualizerCamera(cameraDistance=1.5, + cameraYaw=0, + cameraPitch=-40, + cameraTargetPosition=[0.55, -0.35, 0.2]) + + self.action_space = spaces.Box(low=np.array( + [self.x_low_action, self.y_low_action, self.z_low_action]), + high=np.array([ + self.x_high_action, + self.y_high_action, + self.z_high_action + ]), + dtype=np.float32) + self.observation_space = spaces.Box( + low=np.array([self.x_low_obs, self.y_low_obs, self.z_low_obs]), + high=np.array([self.x_high_obs, self.y_high_obs, self.z_high_obs]), + dtype=np.float32) + self.step_counter = 0 + + self.urdf_root_path = pybullet_data.getDataPath() + # lower limits for null space + self.lower_limits = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05] + # upper limits for null space + self.upper_limits = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05] + # joint ranges for null space + self.joint_ranges = [5.8, 4, 5.8, 4, 5.8, 4, 6] + # restposes for null space + self.rest_poses = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0] + # joint damping coefficents + self.joint_damping = [ + 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001 + ] + + self.init_joint_positions = [ + 0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, + -0.006539 + ] + + self.orientation = p.getQuaternionFromEuler( + [0., -math.pi, math.pi / 2.]) + + self.seed(config.worker_index*config.num_workers) + self.reset() + + def seed(self, seed=None): + random.seed(seed) + + def reset(self): + #p.connect(p.GUI) + self.step_counter = 0 + + p.resetSimulation() + #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) + self.terminated = False + p.setGravity(0, 0, -10) + + #这些是周围那些白线,用来观察是否超过了obs的边界 + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, 0], + lineToXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_high_obs, 0], + lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_low_obs, 0], + lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_high_obs, 0], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) + p.addUserDebugLine( + lineFromXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs], + lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) + + p.loadURDF(os.path.join(self.urdf_root_path, "plane.urdf"), + basePosition=[0, 0, -0.65]) + self.kuka_id = p.loadURDF(os.path.join(self.urdf_root_path, + "kuka_iiwa/model.urdf"), + useFixedBase=True) + p.loadURDF(os.path.join(self.urdf_root_path, "table/table.urdf"), + basePosition=[0.5, 0, -0.65]) + # p.loadURDF(os.path.join(self.urdf_root_path, "tray/traybox.urdf"),basePosition=[0.55,0,0]) + #object_id=p.loadURDF(os.path.join(self.urdf_root_path, "random_urdfs/000/000.urdf"), basePosition=[0.53,0,0.02]) + self.object_id = p.loadURDF(os.path.join(self.urdf_root_path, + "random_urdfs/000/000.urdf"), + basePosition=[ + random.uniform(self.x_low_obs, + self.x_high_obs), + random.uniform(self.y_low_obs, + self.y_high_obs), 0.01 + ]) + + self.num_joints = p.getNumJoints(self.kuka_id) + + for i in range(self.num_joints): + p.resetJointState( + bodyUniqueId=self.kuka_id, + jointIndex=i, + targetValue=self.init_joint_positions[i], + ) + + self.robot_pos_obs = p.getLinkState(self.kuka_id, + self.num_joints - 1)[4] + #logging.debug("init_pos={}\n".format(p.getLinkState(self.kuka_id,self.num_joints-1))) + p.stepSimulation() + # self.object_pos = p.getBasePositionAndOrientation(self.object_id)[0] + # self.robot_pos=p.getLinkState(self.kuka_id,self.num_joints-1)[4] + # return np.array(self.object_pos+self.robot_pos).astype(np.float32) + #return np.array(self.robot_pos_obs).astype(np.float32) + return self._get_observation() + + def step(self, action): + dv = 0.005 + dx = action[0] * dv + dy = action[1] * dv + dz = action[2] * dv + + self.current_pos = p.getLinkState(self.kuka_id, self.num_joints - 1)[4] + # logging.debug("self.current_pos={}\n".format(self.current_pos)) + self.new_robot_pos = [ + self.current_pos[0] + dx, self.current_pos[1] + dy, + self.current_pos[2] + dz + ] + #logging.debug("self.new_robot_pos={}\n".format(self.new_robot_pos)) + self.robot_joint_positions = p.calculateInverseKinematics( + bodyUniqueId=self.kuka_id, + endEffectorLinkIndex=self.num_joints - 1, + targetPosition=[ + self.new_robot_pos[0], self.new_robot_pos[1], + self.new_robot_pos[2] + ], + targetOrientation=self.orientation, + jointDamping=self.joint_damping, + ) + for i in range(self.num_joints): + p.resetJointState( + bodyUniqueId=self.kuka_id, + jointIndex=i, + targetValue=self.robot_joint_positions[i], + ) + p.stepSimulation() + + #在代码开始部分,如果定义了is_good_view,那么机械臂的动作会变慢,方便观察 + if self.is_good_view: + time.sleep(0.05) + + self.step_counter += 1 + return self._reward() + + def _get_observation(self): + self.object_pos = p.getBasePositionAndOrientation(self.object_id)[0] + self.robot_pos=p.getLinkState(self.kuka_id,self.num_joints-1)[4] + return np.array(self.object_pos+self.robot_pos).astype(np.float32) + + def _reward(self): + + #一定注意是取第4个值,请参考pybullet手册的这个函数返回值的说明 + self.robot_state = p.getLinkState(self.kuka_id, self.num_joints - 1)[4] + # self.object_state=p.getBasePositionAndOrientation(self.object_id) + # self.object_state=np.array(self.object_state).astype(np.float32) + # + self.object_state = np.array( + p.getBasePositionAndOrientation(self.object_id)[0]).astype( + np.float32) + + square_dx = (self.robot_state[0] - self.object_state[0])**2 + square_dy = (self.robot_state[1] - self.object_state[1])**2 + square_dz = (self.robot_state[2] - self.object_state[2])**2 + + #用机械臂末端和物体的距离作为奖励函数的依据 + self.distance = sqrt(square_dx + square_dy + square_dz) + #print(self.distance) + + x = self.robot_state[0] + y = self.robot_state[1] + z = self.robot_state[2] + + #如果机械比末端超过了obs的空间,也视为done,而且会给予一定的惩罚 + terminated = bool(x < self.x_low_obs or x > self.x_high_obs + or y < self.y_low_obs or y > self.y_high_obs + or z < self.z_low_obs or z > self.z_high_obs) + + if terminated: + reward = -0.1 + self.terminated = True + + #如果机械臂一直无所事事,在最大步数还不能接触到物体,也需要给一定的惩罚 + elif self.step_counter > self.max_steps_one_episode: + reward = -0.1 + self.terminated = True + + elif self.distance < 0.1: + reward = 1 + self.terminated = True + else: + reward = 0 + self.terminated = False + + info = {'distance:', self.distance} + #self.observation=self.robot_state + self.observation = self.object_state + return self._get_observation(), reward, self.terminated, info + + def close(self): + p.disconnect() + + +if __name__ == '__main__': + env_config={ + "is_render":True, + "is_good_view":True, + "max_steps_one_episode":1000 + } + + config=EnvContext(env_config=env_config,worker_index=1,num_workers=1) + # 这一部分是做baseline,即让机械臂随机选择动作,看看能够得到的分数 + env = KukaReachEnv(config) + print(env) + print(env.observation_space.shape) + # print(env.observation_space.sample()) + # print(env.action_space.sample()) + print(env.action_space.shape) + obs = env.reset() + print(obs) + sum_reward=0 + for i in range(10): + env.reset() + for i in range(2000): + action=env.action_space.sample() + #action=np.array([0,0,0.47-i/1000]) + obs,reward,done,info=env.step(action) + # print("i={},\naction={},\nobs={},\ndone={},\n".format(i,action,obs,done,)) + print('obs=',obs) + print("reward={},info={}".format(reward,info)) + # print(colored("info={}".format(info),"cyan")) + sum_reward+=reward + if done: + break + # time.sleep(0.1) + print() + print(sum_reward) \ No newline at end of file diff --git a/pretrained/grasp/model.pt b/pretrained/grasp/model.pt deleted file mode 100644 index 3f1ddae3ab198fa03b1800b1e2d3a5f0f6b2609d..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 76680 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or y < self.y_low_obs or y > self.y_high_obs - or z < self.z_low_obs or z > self.z_high_obs) - - if terminated: - reward = -0.1 - self.terminated = True - - elif self.step_counter > self.max_steps_one_episode: - reward = -0.1 - self.terminated = True - - elif distance < 0.1: - reward = 1 - self.terminated = True - else: - reward = 0 - self.terminated = False - - info = {'distance:', distance} - - return self._resolve_obs_return(), reward, self.terminated, info - - def close(self): - p.disconnect() - - -class CustomSkipFrame(gym.Wrapper): - """ Make a 4 frame skip, so the observation space will change to (4,84,84) from (1,84,84) - - Args: - gym ([type]): [description] - """ - def __init__(self, env, skip=4): - super(CustomSkipFrame, self).__init__(env) - self.observation_space = spaces.Box(low=0, - high=1, - shape=(skip, 84, 84)) - self.skip = skip - - def step(self, action): - total_reward = 0 - states = [] - state, reward, done, info = self.env.step(action) - for i in range(self.skip): - if not done: - state, reward, done, info = self.env.step(action) - total_reward += reward - states.append(state) - else: - states.append(state) - states = np.concatenate(states, 0)[None, :, :, :] - return states.astype(np.float32), reward, done, info - - def reset(self): - state = self.env.reset() - states = np.concatenate([state for _ in range(self.skip)], - 0)[None, :, :, :] - return states.astype(np.float32) - - -if __name__ == '__main__': - import matplotlib.pyplot as plt - # 这一部分是做baseline,即让机械臂随机选择动作,看看能够得到的分数 - env = KukaCamGripperReachEnv(is_good_view=True,is_render=True) - env=CustomSkipFrame(env) - - print('env={}'.format(env)) - print(env.observation_space.shape) - # print(env.observation_space.sample()) - # print(env.action_space.sample()) - print(env.action_space.shape) - obs = env.reset() - print(Fore.RED + 'obs={}'.format(obs)) - action = env.action_space.sample() - obs, reward, done, info = env.step(action) - print('obs={},reward={},done={},info={}'.format(obs, reward, done, info)) - - - sum_reward = 0 - success_times = 0 - for i in range(100): - env.reset() - for i in range(1000): - action = env.action_space.sample() - obs, reward, done, info = env.step(action) - img = obs[0][0] - plt.imshow(img, cmap='gray') - plt.show() - - print('obs={},reward={},done={},info={}'.format( - obs, reward, done, info)) - sum_reward += reward - if reward == 1: - success_times += 1 - if done: - break - # time.sleep(0.1) - print() - print('sum_reward={}'.format(sum_reward)) - print('success rate={}'.format(success_times / 50)) diff --git a/env/kuka_cam_reach.py b/env/kuka_cam_reach.py deleted file mode 100644 index 228dc81..0000000 --- a/env/kuka_cam_reach.py +++ /dev/null @@ -1,580 +0,0 @@ -#!/usr/bin/env python3 -# -*- encoding: utf-8 -*- -''' -@File : kuka_reach_env.py -@Time : 2021/03/20 14:33:24 -@Author : Yan Wen -@Version : 1.0 -@Contact : z19040042@s.upc.edu.cn -@License : (C)Copyright 2021-2022, Liugroup-NLPR-CASIA -@Desc : None -''' - -# here put the import lib - -import pybullet as p -import pybullet_data -import os -import sys -import gym -from gym import spaces -from gym.utils import seeding -import numpy as np -from math import sqrt -import random -import time -from numpy import arange -import logging - -import math -import cv2 -import torch.nn.functional as F -import torchvision.transforms as T -from PIL import Image -from termcolor import colored -import torch -import matplotlib.pyplot as plt -from colorama import Fore, init, Back -import sys -import os - -init(autoreset=True) # this lets colorama takes effect only in current line. -# Otherwise, colorama will let the sentences below 'print(Fore.GREEN+'xx')' -# all become green color. - -#### 一些变量 ###### -#LOGGING_LEVEL=logging.INFO -# is_render=False -# is_good_view=False #这个的作用是在step时加上time.sleep(),把机械比的动作放慢,看的更清,但是会降低训练速度 -######################### - -# logging.basicConfig( -# level=LOGGING_LEVEL, -# format='%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', -# filename='../logs/reach_env.log'.format(time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())), -# filemode='w') -# logger = logging.getLogger(__name__) -# env_logger=logging.getLogger('env.py') - -# logging模块的使用 -# 级别 何时使用 -# DEBUG 细节信息,仅当诊断问题时适用。 -# INFO 确认程序按预期运行 -# WARNING 表明有已经或即将发生的意外(例如:磁盘空间不足)。程序仍按预期进行 -# ERROR 由于严重的问题,程序的某些功能已经不能正常执行 -# CRITICAL 严重的错误,表明程序已不能继续执行 - - -class KukaCamReachEnv(gym.Env): - metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second': 50 - } - max_steps_one_episode = 2000 - - # final_image_size=40 - # resize = T.Compose([T.ToPILImage(), - # T.Resize(final_image_size, interpolation=Image.CUBIC), - # T.ToTensor()]) - - def __init__(self, is_render=False, is_good_view=False): - - # some camera parameters - # all the parameters are tested with test/slide_bar_for_camera.py file. - - self.camera_parameters = { - 'width': 960., - 'height': 720, - 'fov': 60, - 'near': 0.1, - 'far': 100., - 'eye_position': [0.59, 0, 0.8], - 'target_position': [0.55, 0, 0.05], - 'camera_up_vector': - [1, 0, 0], # I really do not know the parameter's effect. - 'light_direction': [ - 0.5, 0, 1 - ], #the direction is from the light source position to the origin of the world frame. - } - - self.device = torch.device( - "cpu" if torch.cuda.is_available() else "cpu") - - # self.view_matrix=p.computeViewMatrix( - # cameraEyePosition=self.camera_parameters['eye_position'], - # cameraTargetPosition=self.camera_parameters['target_position'], - # cameraUpVector=self.camera_parameters['camera_up_vector'] - # ) - self.view_matrix = p.computeViewMatrixFromYawPitchRoll( - cameraTargetPosition=[0.55, 0, 0.05], - distance=.7, - yaw=90, - pitch=-70, - roll=0, - upAxisIndex=2) - - self.projection_matrix = p.computeProjectionMatrixFOV( - fov=self.camera_parameters['fov'], - aspect=self.camera_parameters['width'] / - self.camera_parameters['height'], - nearVal=self.camera_parameters['near'], - farVal=self.camera_parameters['far']) - - self.is_render = is_render - self.is_good_view = is_good_view - - if self.is_render: - p.connect(p.GUI) - else: - p.connect(p.DIRECT) - - self.x_low_obs = 0.2 - self.x_high_obs = 0.7 - self.y_low_obs = -0.3 - self.y_high_obs = 0.3 - self.z_low_obs = 0 - self.z_high_obs = 0.55 - - self.x_low_action = -0.4 - self.x_high_action = 0.4 - self.y_low_action = -0.4 - self.y_high_action = 0.4 - self.z_low_action = -0.6 - self.z_high_action = 0.3 - - p.configureDebugVisualizer(lightPosition=[5, 0, 5]) - p.resetDebugVisualizerCamera(cameraDistance=1.5, - cameraYaw=0, - cameraPitch=-40, - cameraTargetPosition=[0.55, -0.35, 0.2]) - - self.action_space = spaces.Box(low=np.array( - [self.x_low_action, self.y_low_action, self.z_low_action]), - high=np.array([ - self.x_high_action, - self.y_high_action, - self.z_high_action - ]), - dtype=np.float32) - # self.observation_space=spaces.Box(low=np.array([self.x_low_obs,self.y_low_obs,self.z_low_obs]), - # high=np.array([self.x_high_obs,self.y_high_obs,self.z_high_obs]), - # dtype=np.float32) - - self.observation_space = spaces.Box(low=0, high=1, shape=(1, 84, 84)) - - self.step_counter = 0 - - self.urdf_root_path = pybullet_data.getDataPath() - # lower limits for null space - self.lower_limits = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05] - # upper limits for null space - self.upper_limits = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05] - # joint ranges for null space - self.joint_ranges = [5.8, 4, 5.8, 4, 5.8, 4, 6] - # restposes for null space - self.rest_poses = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0] - # joint damping coefficents - self.joint_damping = [ - 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001 - ] - - self.init_joint_positions = [ - 0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, - -0.006539 - ] - - self.orientation = p.getQuaternionFromEuler( - [0., -math.pi, math.pi / 2.]) - - self.seed() - self.reset() - - def seed(self, seed=None): - self.np_random, seed = seeding.np_random(seed) - return [seed] - - def reset(self): - #p.connect(p.GUI) - self.step_counter = 0 - - p.resetSimulation() - #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) - self.terminated = False - p.setGravity(0, 0, -10) - - #这些是周围那些白线,用来观察是否超过了obs的边界 - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_low_obs, 0], - lineToXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_high_obs, 0], - lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_high_obs, self.y_low_obs, 0], - lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_high_obs, self.y_high_obs, 0], - lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) - - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], - lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs], - lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], - lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs], - lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) - - p.loadURDF(os.path.join(self.urdf_root_path, "plane.urdf"), - basePosition=[0, 0, -0.65]) - self.kuka_id = p.loadURDF(os.path.join(self.urdf_root_path, - "kuka_iiwa/model.urdf"), - useFixedBase=True) - table_uid = p.loadURDF(os.path.join(self.urdf_root_path, - "table/table.urdf"), - basePosition=[0.5, 0, -0.65]) - p.changeVisualShape(table_uid, -1, rgbaColor=[1, 1, 1, 1]) - # p.loadURDF(os.path.join(self.urdf_root_path, "tray/traybox.urdf"),basePosition=[0.55,0,0]) - #object_id=p.loadURDF(os.path.join(self.urdf_root_path, "random_urdfs/000/000.urdf"), basePosition=[0.53,0,0.02]) - self.object_id = p.loadURDF(os.path.join(self.urdf_root_path, - "random_urdfs/000/000.urdf"), - basePosition=[ - random.uniform(self.x_low_obs, - self.x_high_obs), - random.uniform(self.y_low_obs, - self.y_high_obs), 0.01 - ]) - - self.num_joints = p.getNumJoints(self.kuka_id) - - for i in range(self.num_joints): - p.resetJointState( - bodyUniqueId=self.kuka_id, - jointIndex=i, - targetValue=self.init_joint_positions[i], - ) - - self.robot_pos_obs = p.getLinkState(self.kuka_id, - self.num_joints - 1)[4] - #logging.debug("init_pos={}\n".format(p.getLinkState(self.kuka_id,self.num_joints-1))) - p.stepSimulation() - - # (_,_,self.images,_,_) = p.getCameraImage( - # width=self.camera_parameters['width'], - # height=self.camera_parameters['height'], - # viewMatrix=self.view_matrix, - # projectionMatrix=self.projection_matrix, - - # renderer=p.ER_BULLET_HARDWARE_OPENGL - # ) - - (_, _, px, _, - _) = p.getCameraImage(width=960, - height=960, - viewMatrix=self.view_matrix, - projectionMatrix=self.projection_matrix, - renderer=p.ER_BULLET_HARDWARE_OPENGL) - self.images = px - - p.enableJointForceTorqueSensor(bodyUniqueId=self.kuka_id, - jointIndex=self.num_joints - 1, - enableSensor=True) - - # print(Fore.GREEN+'force_sensor={}'.format(self._get_force_sensor_value())) - - self.object_pos = p.getBasePositionAndOrientation(self.object_id)[0] - #return np.array(self.object_pos).astype(np.float32) - #return np.array(self.robot_pos_obs).astype(np.float32) - self.images = self.images[:, :, : - 3] # the 4th channel is alpha channel, we do not need it. - #return self._process_image(self.images) - - return self._process_image(self.images) - - def _process_image(self, image): - """Convert the RGB pic to gray pic and add a channel 1 - - Args: - image ([type]): [description] - """ - - if image is not None: - image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) - image = cv2.resize(image, (84, 84))[None, :, :] / 255. - return image - else: - return np.zeros((1, 84, 84)) - - # image=image.transpose((2,0,1)) - # image=np.ascontiguousarray(image,dtype=np.float32)/255. - # image=torch.from_numpy(image) - # #self.processed_image=self.resize(image).unsqueeze(0).to(self.device) - # self.processed_image=self.resize(image).to(self.device) - # return self.processed_image - - - def step(self, action): - dv = 0.005 - dx = action[0] * dv - dy = action[1] * dv - dz = action[2] * dv - - self.current_pos = p.getLinkState(self.kuka_id, self.num_joints - 1)[4] - # logging.debug("self.current_pos={}\n".format(self.current_pos)) - self.new_robot_pos = [ - self.current_pos[0] + dx, self.current_pos[1] + dy, - self.current_pos[2] + dz - ] - #logging.debug("self.new_robot_pos={}\n".format(self.new_robot_pos)) - self.robot_joint_positions = p.calculateInverseKinematics( - bodyUniqueId=self.kuka_id, - endEffectorLinkIndex=self.num_joints - 1, - targetPosition=[ - self.new_robot_pos[0], self.new_robot_pos[1], - self.new_robot_pos[2] - ], - targetOrientation=self.orientation, - jointDamping=self.joint_damping, - ) - for i in range(self.num_joints): - p.resetJointState( - bodyUniqueId=self.kuka_id, - jointIndex=i, - targetValue=self.robot_joint_positions[i], - ) - p.stepSimulation() - - #在代码开始部分,如果定义了is_good_view,那么机械臂的动作会变慢,方便观察 - if self.is_good_view: - time.sleep(0.05) - - self.step_counter += 1 - - return self._reward() - - def _reward(self): - - #一定注意是取第4个值,请参考pybullet手册的这个函数返回值的说明 - self.robot_state = p.getLinkState(self.kuka_id, self.num_joints - 1)[4] - # self.object_state=p.getBasePositionAndOrientation(self.object_id) - # self.object_state=np.array(self.object_state).astype(np.float32) - # - self.object_state = np.array( - p.getBasePositionAndOrientation(self.object_id)[0]).astype( - np.float32) - - square_dx = (self.robot_state[0] - self.object_state[0])**2 - square_dy = (self.robot_state[1] - self.object_state[1])**2 - square_dz = (self.robot_state[2] - self.object_state[2])**2 - - #用机械臂末端和物体的距离作为奖励函数的依据 - self.distance = sqrt(square_dx + square_dy + square_dz) - #print(self.distance) - - x = self.robot_state[0] - y = self.robot_state[1] - z = self.robot_state[2] - - #如果机械比末端超过了obs的空间,也视为done,而且会给予一定的惩罚 - terminated = bool(x < self.x_low_obs or x > self.x_high_obs - or y < self.y_low_obs or y > self.y_high_obs - or z < self.z_low_obs or z > self.z_high_obs) - - if terminated: - reward = -0.1 - self.terminated = True - - #如果机械臂一直无所事事,在最大步数还不能接触到物体,也需要给一定的惩罚 - elif self.step_counter > self.max_steps_one_episode: - reward = -0.1 - self.terminated = True - - elif self.distance < 0.1: - reward = 1 - self.terminated = True - else: - reward = 0 - self.terminated = False - - info = {'distance:', self.distance} - (_, _, px, _, - _) = p.getCameraImage(width=960, - height=960, - viewMatrix=self.view_matrix, - projectionMatrix=self.projection_matrix, - renderer=p.ER_BULLET_HARDWARE_OPENGL) - self.images = px - self.processed_image = self._process_image(self.images) - #self.observation=self.robot_state - self.observation = self.object_state - return self.processed_image, reward, self.terminated, info - - def close(self): - p.disconnect() - - def run_for_debug(self, target_position): - temp_robot_joint_positions = p.calculateInverseKinematics( - bodyUniqueId=self.kuka_id, - endEffectorLinkIndex=self.num_joints - 1, - targetPosition=target_position, - targetOrientation=self.orientation, - jointDamping=self.joint_damping, - ) - for i in range(self.num_joints): - p.resetJointState( - bodyUniqueId=self.kuka_id, - jointIndex=i, - targetValue=temp_robot_joint_positions[i], - ) - p.stepSimulation() - - #在代码开始部分,如果定义了is_good_view,那么机械臂的动作会变慢,方便观察 - if self.is_good_view: - time.sleep(0.05) - - return self._get_force_sensor_value() - - def _get_force_sensor_value(self): - force_sensor_value = p.getJointState(bodyUniqueId=self.kuka_id, - jointIndex=self.num_joints - - 1)[2][2] - # the first 2 stands for jointReactionForces, the second 2 stands for Fz, - # the pybullet methods' return is a tuple,so can not - # index it with str like dict. I think it can be improved - # that return value is a dict rather than tuple. - return force_sensor_value - - -class CustomSkipFrame(gym.Wrapper): - """ Make a 4 frame skip, so the observation space will change to (4,84,84) from (1,84,84) - - Args: - gym ([type]): [description] - """ - def __init__(self, env, skip=4): - super(CustomSkipFrame, self).__init__(env) - self.observation_space = spaces.Box(low=0, - high=1, - shape=(skip, 84, 84)) - self.skip = skip - - def step(self, action): - total_reward = 0 - states = [] - state, reward, done, info = self.env.step(action) - for i in range(self.skip): - if not done: - state, reward, done, info = self.env.step(action) - total_reward += reward - states.append(state) - else: - states.append(state) - states = np.concatenate(states, 0)[None, :, :, :] - return states.astype(np.float32), reward, done, info - - def reset(self): - state = self.env.reset() - states = np.concatenate([state for _ in range(self.skip)], - 0)[None, :, :, :] - return states.astype(np.float32) - - -if __name__ == '__main__': - # 这一部分是做baseline,即让机械臂随机选择动作,看看能够得到的分数 - import matplotlib.pyplot as plt - env = KukaCamReachEnv(is_good_view=True, is_render=False) - env = CustomSkipFrame(env) - - obs = env.reset() - print(obs) - print(obs.shape) - - img = obs[0][0] - plt.imshow(img, cmap='gray') - plt.show() - - # all the below are some debug codes, if you have interests, look through. - - # b=a[:,:,:3] - # c=b.transpose((2,0,1)) - # #c=b - # d=np.ascontiguousarray(c,dtype=np.float32)/255 - # e=torch.from_numpy(d) - # resize=T.Compose([T.ToPILImage(), - # T.Resize(40,interpolation=Image.CUBIC), - # T.ToTensor()]) - - # f=resize(e).unsqueeze(0) - # #print(f) - # # g=f.unsqueeze(0) - # # print(g) - # #f.transpose((2,0,1)) - - # plt.imshow(f.cpu().squeeze(0).permute(1, 2, 0).numpy(), - # interpolation='none') - - # #plt.imshow(f) - # plt.show() - - # resize = T.Compose([T.ToPILImage(), - # T.Resize(40, interpolation=Image.CUBIC), - # T.ToTensor()]) - -# print(env) -# print(env.observation_space.shape) -# print(env.observation_space.sample()) - -# for i in range(10): -# a=env.reset() -# b=a[:,:,:3] -# """ -# matplotlib.pyplot.imshow(X, cmap=None, norm=None, aspect=None, interpolation=None, -# alpha=None, vmin=None, vmax=None, origin=None, extent=None, *, filternorm=True, -# filterrad=4.0, resample=None, url=None, data=None, **kwargs) - -# Xarray-like or PIL image -# The image data. Supported array shapes are: - -# (M, N): an image with scalar data. The values are mapped to colors using normalization and a colormap. See parameters norm, cmap, vmin, vmax. -# (M, N, 3): an image with RGB values (0-1 float or 0-255 int). -# (M, N, 4): an image with RGBA values (0-1 float or 0-255 int), i.e. including transparency. -# The first two dimensions (M, N) define the rows and columns of the image. -# Out-of-range RGB(A) values are clipped. -# """ -# plt.imshow(b) -# plt.show() -# time.sleep(1) - -# for i in range(720): -# for j in range(720): -# for k in range(3): -# if not a[i][j][k]==b[i][j][k]: -# print(Fore.RED+'there is unequal') -# raise ValueError('there is unequal.') -# print('check complete') - -#print(a) -#force_sensor=env.run_for_debug([0.6,0.0,0.03]) -# print(Fore.RED+'after force sensor={}'.format(force_sensor)) -#print(env.action_space.sample()) - -# sum_reward=0 -# for i in range(10): -# env.reset() -# for i in range(2000): -# action=env.action_space.sample() -# #action=np.array([0,0,0.47-i/1000]) -# obs,reward,done,info=env.step(action) -# # print("i={},\naction={},\nobs={},\ndone={},\n".format(i,action,obs,done,)) -# print(colored("reward={},info={}".format(reward,info),"cyan")) -# # print(colored("info={}".format(info),"cyan")) -# sum_reward+=reward -# if done: -# break -# # time.sleep(0.1) -# print() -# print(sum_reward) diff --git a/env/kuka_gripper_reach_env.py b/env/kuka_gripper_reach_env.py deleted file mode 100644 index 6ac131a..0000000 --- a/env/kuka_gripper_reach_env.py +++ /dev/null @@ -1,353 +0,0 @@ -#!/usr/bin/env python3 -# -*- encoding: utf-8 -*- -''' -@File : kuka_base_env.py -@Time : 2021/04/19 21:42:15 -@Author : Yan Wen -@Version : 1.0 -@Contact : z19040042@s.upc.edu.cn -@Desc : None -''' - -# here put the import lib -import logging -import pybullet as p -import pybullet_data -import os -import gym -from gym import spaces -from gym.utils import seeding -import numpy as np -from math import sqrt -import random -import time -import math -from colorama import Fore, Back, init - -init(autoreset=True) -import sys - -# logging.basicConfig( -# level=logging.INFO, -# format='%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s', -# #filename='./logs/client1-{}.log'.format(time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())), -# #filemode='w') -# ) - -logger = logging.getLogger(__name__) - -formatter = logging.Formatter( - '%(asctime)s - %(threadName)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s' -) - -stream_handler = logging.StreamHandler() - -stream_handler.setLevel(logging.INFO) -stream_handler.setFormatter(formatter) -logger.addHandler(stream_handler) - - -class KukaGripperReachEnv(gym.Env): - metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second': 50 - } - max_steps_one_episode = 1000 - - def __init__(self, is_render=False, is_good_view=False): - - self.is_render = is_render - self.is_good_view = is_good_view - - if self.is_render: - p.connect(p.GUI) - else: - p.connect(p.DIRECT) - - self.gripper_length = 0.257 - - self.x_low_obs = 0.2 - self.x_high_obs = 0.7 - self.y_low_obs = -0.3 - self.y_high_obs = 0.3 - self.z_low_obs = 0 - self.z_high_obs = 0.55 - - self.x_low_action = -0.4 - self.x_high_action = 0.4 - self.y_low_action = -0.4 - self.y_high_action = 0.4 - self.z_low_action = -0.6 - self.z_high_action = 0.3 - - p.resetDebugVisualizerCamera(cameraDistance=1.5, - cameraYaw=0, - cameraPitch=-40, - cameraTargetPosition=[0.55, -0.35, 0.2]) - - self.action_space = spaces.Box(low=np.array( - [self.x_low_action, self.y_low_action, self.z_low_action]), - high=np.array([ - self.x_high_action, - self.y_high_action, - self.z_high_action - ]), - dtype=np.float32) - - self.observation_space = spaces.Box( - low=np.array([ - self.x_low_obs, self.y_low_obs, self.z_low_obs, self.x_low_obs, - self.y_low_obs, self.z_low_obs + self.gripper_length - ]), - high=np.array([ - self.x_high_obs, self.y_high_obs, self.z_high_obs, - self.x_high_obs, self.y_high_obs, - self.z_high_obs + self.gripper_length - ]), - dtype=np.float32) - - self.step_counter = 0 - - self.end_effector_index = 6 - self.gripper_index = 7 - - self.urdf_root_path = pybullet_data.getDataPath() - # lower limits for null space - self.lower_limits = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05] - # upper limits for null space - self.upper_limits = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05] - # joint ranges for null space - self.joint_ranges = [5.8, 4, 5.8, 4, 5.8, 4, 6] - # restposes for null space - self.rest_poses = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0] - # joint damping coefficents - self.joint_damping = [ - 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, - 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001 - ] - - self.init_joint_positions = [ - 0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, - -0.006539, 0.000048, -0.299912, 0.000000, -0.000043, 0.299960, - 0.000000, -0.000200 - ] - - self.orientation = p.getQuaternionFromEuler([math.pi, 0, math.pi]) - - self.seed() - self.reset() - - def seed(self, seed=None): - self.np_random, seed = seeding.np_random(seed) - return [seed] - - def reset(self): - #p.connect(p.GUI) - self.step_counter = 0 - - p.resetSimulation() - #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) - self.terminated = False - p.setGravity(0, 0, -10) - - #这些是周围那些白线,用来观察是否超过了obs的边界 - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_low_obs, 0], - lineToXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_high_obs, 0], - lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_high_obs, self.y_low_obs, 0], - lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_high_obs, self.y_high_obs, 0], - lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) - - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], - lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs], - lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], - lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs], - lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) - - p.loadURDF(os.path.join(self.urdf_root_path, "plane.urdf"), - basePosition=[0, 0, -0.65]) - - self.kuka_id = p.loadSDF( - os.path.join(self.urdf_root_path, - "kuka_iiwa/kuka_with_gripper2.sdf"))[0] - # The return of loadSDF is a tuple (0,) - - table_uid = p.loadURDF(os.path.join(self.urdf_root_path, - "table/table.urdf"), - basePosition=[0.5, 0, -0.65]) - p.changeVisualShape(table_uid, -1, rgbaColor=[1, 1, 1, 1]) - - self.object_id = p.loadURDF( - os.path.join(self.urdf_root_path, "random_urdfs/000/000.urdf"), - basePosition=[ - random.uniform(self.x_low_obs, self.x_high_obs), - random.uniform(self.y_low_obs, self.y_high_obs), 0.01 - ], - baseOrientation=p.getQuaternionFromEuler([0, 0, 0])) - - for i in range(p.getNumJoints(self.kuka_id)): - p.resetJointState( - bodyUniqueId=self.kuka_id, - jointIndex=i, - targetValue=self.init_joint_positions[i], - ) - - p.resetJointState(self.kuka_id, 8, -0.3) - p.resetJointState(self.kuka_id, 10, 0) - p.resetJointState(self.kuka_id, 11, 0.3) - p.resetJointState(self.kuka_id, 13, 0) - - p.stepSimulation() - return self._resolve_obs_return() - - def _resolve_obs_return(self): - object_position = list( - p.getBasePositionAndOrientation(self.object_id)[0]) - robot_end_effector_position = list( - p.getLinkState(self.kuka_id, self.end_effector_index)[4]) - - logger.debug(Fore.BLUE + 'object_position={}'.format(object_position)) - logger.debug(Fore.YELLOW + 'robot end effector position={}'.format( - robot_end_effector_position)) - - return np.array(object_position + robot_end_effector_position).astype( - np.float32) - - def step(self, action): - dv = 0.005 - dx = action[0] * dv - dy = action[1] * dv - dz = action[2] * dv - - current_pos = np.array( - p.getLinkState(self.kuka_id, - self.end_effector_index)[4]).astype(np.float32) - - new_robot_pos = [ - current_pos[0] + dx, current_pos[1] + dy, current_pos[2] + dz - ] - - robot_joint_positions = p.calculateInverseKinematics( - bodyUniqueId=self.kuka_id, - endEffectorLinkIndex=self.end_effector_index, - targetPosition=[ - new_robot_pos[0], new_robot_pos[1], new_robot_pos[2] - ], - targetOrientation=self.orientation, - jointDamping=self.joint_damping, - ) - - for i in range(self.end_effector_index): - p.resetJointState( - bodyUniqueId=self.kuka_id, - jointIndex=i, - targetValue=robot_joint_positions[i], - ) - p.stepSimulation() - - if self.is_good_view: - time.sleep(0.05) - - self.step_counter += 1 - return self._reward() - - def _reward(self): - - robot_end_effector_position = np.array( - p.getLinkState(self.kuka_id, - self.end_effector_index)[4]).astype(np.float32) - - # We got the position of the robot end effector not gripper end effector, - # so we need to compasent the gripper length - # What we want is the gripper end effector position - robot_end_effector_position[2] -= self.gripper_length - object_position = np.array( - p.getBasePositionAndOrientation(self.object_id)[0]).astype( - np.float32) - - square_dx = np.float32( - (robot_end_effector_position[0] - object_position[0])**2) - square_dy = np.float32( - (robot_end_effector_position[1] - object_position[1])**2) - square_dz = np.float32( - (robot_end_effector_position[2] - object_position[2])**2) - - distance = np.float32(sqrt(square_dx + square_dy + square_dz)) - - x = np.float32(robot_end_effector_position[0]) - y = np.float32(robot_end_effector_position[1]) - z = np.float32(robot_end_effector_position[2]) - logger.debug(Fore.RED + 'x,y,z={},{},{}'.format(x, y, z)) - - terminated = bool(x < self.x_low_obs or x > self.x_high_obs - or y < self.y_low_obs or y > self.y_high_obs - or z < self.z_low_obs or z > self.z_high_obs) - - if terminated: - reward = -0.1 - self.terminated = True - - elif self.step_counter > self.max_steps_one_episode: - reward = -0.1 - self.terminated = True - - elif distance < 0.1: - reward = 1 - self.terminated = True - else: - reward = 0 - self.terminated = False - - info = {'distance:', distance} - - return self._resolve_obs_return(), reward, self.terminated, info - - def close(self): - p.disconnect() - - -if __name__ == '__main__': - # 这一部分是做baseline,即让机械臂随机选择动作,看看能够得到的分数 - env = KukaGripperReachEnv(is_good_view=True,is_render=True) - print('env={}'.format(env)) - print(env.observation_space.shape) - # print(env.observation_space.sample()) - # print(env.action_space.sample()) - print(env.action_space.shape) - obs = env.reset() - print(Fore.RED + 'obs={}'.format(obs)) - action = env.action_space.sample() - obs, reward, done, info = env.step(action) - print('obs={},reward={},done={},info={}'.format(obs, reward, done, info)) - - sum_reward = 0 - success_times = 0 - for i in range(100): - env.reset() - for i in range(1000): - action = env.action_space.sample() - obs, reward, done, info = env.step(action) - print('obs={},reward={},done={},info={}'.format( - obs, reward, done, info)) - sum_reward += reward - if reward == 1: - success_times += 1 - if done: - break - # time.sleep(0.1) - print() - print('sum_reward={}'.format(sum_reward)) - print('success rate={}'.format(success_times / 50)) diff --git a/env/kuka_reach_env.py b/env/kuka_reach_env.py deleted file mode 100644 index 8b4c125..0000000 --- a/env/kuka_reach_env.py +++ /dev/null @@ -1,311 +0,0 @@ -#!/usr/bin/env python3 -# -*- encoding: utf-8 -*- -''' -@File : kuka_reach_env.py -@Time : 2021/03/20 14:33:24 -@Author : Yan Wen -@Version : 1.0 -@Contact : z19040042@s.upc.edu.cn -@Desc : None -''' - -# here put the import lib - -import pybullet as p -import pybullet_data -import os -import sys -import gym -from gym import spaces -from gym.utils import seeding -import numpy as np -from math import sqrt -import random -import time -from numpy import arange -import logging -import math - -import ray -from ray import tune -from ray.tune import grid_search -from ray.rllib.env.env_context import EnvContext -from ray.rllib.models import ModelCatalog -from ray.rllib.models.tf.tf_modelv2 import TFModelV2 -from ray.rllib.models.tf.fcnet import FullyConnectedNetwork -from ray.rllib.models.torch.torch_modelv2 import TorchModelV2 -from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC -from ray.rllib.utils.framework import try_import_tf, try_import_torch -from ray.rllib.utils.test_utils import check_learning_achieved - -tf1, tf, tfv = try_import_tf() -torch, nn = try_import_torch() - -class KukaReachEnv(gym.Env): - metadata = { - 'render.modes': ['human', 'rgb_array'], - 'video.frames_per_second': 50 - } - - def __init__(self, config:EnvContext): - - self.max_steps_one_episode=if config["max_steps_one_episode"] else 1000 - self.is_render = if config["is_render"] else False - self.is_good_view = if config["is_good_view"] else False - - if self.is_render: - p.connect(p.GUI) - else: - p.connect(p.DIRECT) - - self.x_low_obs = 0.2 - self.x_high_obs = 0.7 - self.y_low_obs = -0.3 - self.y_high_obs = 0.3 - self.z_low_obs = 0 - self.z_high_obs = 0.55 - - self.x_low_action = -0.4 - self.x_high_action = 0.4 - self.y_low_action = -0.4 - self.y_high_action = 0.4 - self.z_low_action = -0.6 - self.z_high_action = 0.3 - - p.resetDebugVisualizerCamera(cameraDistance=1.5, - cameraYaw=0, - cameraPitch=-40, - cameraTargetPosition=[0.55, -0.35, 0.2]) - - self.action_space = spaces.Box(low=np.array( - [self.x_low_action, self.y_low_action, self.z_low_action]), - high=np.array([ - self.x_high_action, - self.y_high_action, - self.z_high_action - ]), - dtype=np.float32) - self.observation_space = spaces.Box( - low=np.array([self.x_low_obs, self.y_low_obs, self.z_low_obs]), - high=np.array([self.x_high_obs, self.y_high_obs, self.z_high_obs]), - dtype=np.float32) - self.step_counter = 0 - - self.urdf_root_path = pybullet_data.getDataPath() - # lower limits for null space - self.lower_limits = [-.967, -2, -2.96, 0.19, -2.96, -2.09, -3.05] - # upper limits for null space - self.upper_limits = [.967, 2, 2.96, 2.29, 2.96, 2.09, 3.05] - # joint ranges for null space - self.joint_ranges = [5.8, 4, 5.8, 4, 5.8, 4, 6] - # restposes for null space - self.rest_poses = [0, 0, 0, 0.5 * math.pi, 0, -math.pi * 0.5 * 0.66, 0] - # joint damping coefficents - self.joint_damping = [ - 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001, 0.00001 - ] - - self.init_joint_positions = [ - 0.006418, 0.413184, -0.011401, -1.589317, 0.005379, 1.137684, - -0.006539 - ] - - self.orientation = p.getQuaternionFromEuler( - [0., -math.pi, math.pi / 2.]) - - self.seed(config.worker_index*config.num_workers) - self.reset() - - def seed(self, seed=None): - random.seed(seed) - - def reset(self): - #p.connect(p.GUI) - self.step_counter = 0 - - p.resetSimulation() - #p.configureDebugVisualizer(p.COV_ENABLE_RENDERING, 0) - self.terminated = False - p.setGravity(0, 0, -10) - - #这些是周围那些白线,用来观察是否超过了obs的边界 - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_low_obs, 0], - lineToXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_high_obs, 0], - lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_high_obs, self.y_low_obs, 0], - lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_high_obs, self.y_high_obs, 0], - lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) - - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], - lineToXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs], - lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_low_obs, self.y_low_obs, self.z_high_obs], - lineToXYZ=[self.x_low_obs, self.y_high_obs, self.z_high_obs]) - p.addUserDebugLine( - lineFromXYZ=[self.x_high_obs, self.y_low_obs, self.z_high_obs], - lineToXYZ=[self.x_high_obs, self.y_high_obs, self.z_high_obs]) - - p.loadURDF(os.path.join(self.urdf_root_path, "plane.urdf"), - basePosition=[0, 0, -0.65]) - self.kuka_id = p.loadURDF(os.path.join(self.urdf_root_path, - "kuka_iiwa/model.urdf"), - useFixedBase=True) - p.loadURDF(os.path.join(self.urdf_root_path, "table/table.urdf"), - basePosition=[0.5, 0, -0.65]) - # p.loadURDF(os.path.join(self.urdf_root_path, "tray/traybox.urdf"),basePosition=[0.55,0,0]) - #object_id=p.loadURDF(os.path.join(self.urdf_root_path, "random_urdfs/000/000.urdf"), basePosition=[0.53,0,0.02]) - self.object_id = p.loadURDF(os.path.join(self.urdf_root_path, - "random_urdfs/000/000.urdf"), - basePosition=[ - random.uniform(self.x_low_obs, - self.x_high_obs), - random.uniform(self.y_low_obs, - self.y_high_obs), 0.01 - ]) - - self.num_joints = p.getNumJoints(self.kuka_id) - - for i in range(self.num_joints): - p.resetJointState( - bodyUniqueId=self.kuka_id, - jointIndex=i, - targetValue=self.init_joint_positions[i], - ) - - self.robot_pos_obs = p.getLinkState(self.kuka_id, - self.num_joints - 1)[4] - #logging.debug("init_pos={}\n".format(p.getLinkState(self.kuka_id,self.num_joints-1))) - p.stepSimulation() - self.object_pos = p.getBasePositionAndOrientation(self.object_id)[0] - self.robot_pos=p.getLinkState(self.kuka_id,self.num_joints-1) - return np.array(self.object_pos).astype(np.float32) - #return np.array(self.robot_pos_obs).astype(np.float32) - - def step(self, action): - dv = 0.005 - dx = action[0] * dv - dy = action[1] * dv - dz = action[2] * dv - - self.current_pos = p.getLinkState(self.kuka_id, self.num_joints - 1)[4] - # logging.debug("self.current_pos={}\n".format(self.current_pos)) - self.new_robot_pos = [ - self.current_pos[0] + dx, self.current_pos[1] + dy, - self.current_pos[2] + dz - ] - #logging.debug("self.new_robot_pos={}\n".format(self.new_robot_pos)) - self.robot_joint_positions = p.calculateInverseKinematics( - bodyUniqueId=self.kuka_id, - endEffectorLinkIndex=self.num_joints - 1, - targetPosition=[ - self.new_robot_pos[0], self.new_robot_pos[1], - self.new_robot_pos[2] - ], - targetOrientation=self.orientation, - jointDamping=self.joint_damping, - ) - for i in range(self.num_joints): - p.resetJointState( - bodyUniqueId=self.kuka_id, - jointIndex=i, - targetValue=self.robot_joint_positions[i], - ) - p.stepSimulation() - - #在代码开始部分,如果定义了is_good_view,那么机械臂的动作会变慢,方便观察 - if self.is_good_view: - time.sleep(0.05) - - self.step_counter += 1 - return self._reward() - - def _reward(self): - - #一定注意是取第4个值,请参考pybullet手册的这个函数返回值的说明 - self.robot_state = p.getLinkState(self.kuka_id, self.num_joints - 1)[4] - # self.object_state=p.getBasePositionAndOrientation(self.object_id) - # self.object_state=np.array(self.object_state).astype(np.float32) - # - self.object_state = np.array( - p.getBasePositionAndOrientation(self.object_id)[0]).astype( - np.float32) - - square_dx = (self.robot_state[0] - self.object_state[0])**2 - square_dy = (self.robot_state[1] - self.object_state[1])**2 - square_dz = (self.robot_state[2] - self.object_state[2])**2 - - #用机械臂末端和物体的距离作为奖励函数的依据 - self.distance = sqrt(square_dx + square_dy + square_dz) - #print(self.distance) - - x = self.robot_state[0] - y = self.robot_state[1] - z = self.robot_state[2] - - #如果机械比末端超过了obs的空间,也视为done,而且会给予一定的惩罚 - terminated = bool(x < self.x_low_obs or x > self.x_high_obs - or y < self.y_low_obs or y > self.y_high_obs - or z < self.z_low_obs or z > self.z_high_obs) - - if terminated: - reward = -0.1 - self.terminated = True - - #如果机械臂一直无所事事,在最大步数还不能接触到物体,也需要给一定的惩罚 - elif self.step_counter > self.max_steps_one_episode: - reward = -0.1 - self.terminated = True - - elif self.distance < 0.1: - reward = 1 - self.terminated = True - else: - reward = 0 - self.terminated = False - - info = {'distance:', self.distance} - #self.observation=self.robot_state - self.observation = self.object_state - return np.array(self.observation).astype( - np.float32), reward, self.terminated, info - - def close(self): - p.disconnect() - - -if __name__ == '__main__': - # 这一部分是做baseline,即让机械臂随机选择动作,看看能够得到的分数 - env = KukaReachEnv(is_render=True,is_good_view=True) - print(env) - print(env.observation_space.shape) - # print(env.observation_space.sample()) - # print(env.action_space.sample()) - print(env.action_space.shape) - obs = env.reset() - print(obs) - sum_reward=0 - for i in range(10): - env.reset() - for i in range(2000): - action=env.action_space.sample() - #action=np.array([0,0,0.47-i/1000]) - obs,reward,done,info=env.step(action) - # print("i={},\naction={},\nobs={},\ndone={},\n".format(i,action,obs,done,)) - print(colored("reward={},info={}".format(reward,info),"cyan")) - # print(colored("info={}".format(info),"cyan")) - sum_reward+=reward - if done: - break - # time.sleep(0.1) - print() - print(sum_reward) \ No newline at end of file

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