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run.py
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import os
import time
import torch
import numpy as np
from config import Config, build_env
from agent import ReplayBuffer
def train_agent(args: Config):
args.init_before_training()
env = build_env(args.env_class, args.env_args)
agent = args.agent_class(args.net_dims, args.state_dim, args.action_dim, gpu_id=args.gpu_id, args=args)
agent.last_state = env.reset()
evaluator = Evaluator(eval_env=build_env(args.env_class, args.env_args),
eval_per_step=args.eval_per_step,
eval_times=args.eval_times,
cwd=args.cwd)
if args.if_off_policy:
buffer = ReplayBuffer(gpu_id=args.gpu_id,
max_size=args.buffer_size,
state_dim=args.state_dim,
action_dim=1 if args.if_discrete else args.action_dim, )
buffer_items = agent.explore_env(env, args.horizon_len * args.eval_times, if_random=True)
buffer.update(buffer_items) # warm up for ReplayBuffer
else:
buffer = []
'''start training'''
cwd = args.cwd
break_step = args.break_step
horizon_len = args.horizon_len
if_off_policy = args.if_off_policy
del args
torch.set_grad_enabled(False)
while True:
buffer_items = agent.explore_env(env, horizon_len)
if if_off_policy:
buffer.update(buffer_items)
else:
buffer[:] = buffer_items
torch.set_grad_enabled(True)
logging_tuple = agent.update_net(buffer)
torch.set_grad_enabled(False)
evaluator.evaluate_and_save(agent.act, horizon_len, logging_tuple)
if (evaluator.total_step > break_step) or os.path.exists(f"{cwd}/stop"):
break # stop training when reach `break_step` or `mkdir cwd/stop`
evaluator.close()
def render_agent(env_class, env_args: dict, net_dims: [int], agent_class, actor_path: str, render_times: int = 8):
env = build_env(env_class, env_args)
state_dim = env_args['state_dim']
action_dim = env_args['action_dim']
agent = agent_class(net_dims, state_dim, action_dim, gpu_id=-1)
actor = agent.act
del agent
print(f"| render and load actor from: {actor_path}")
actor.load_state_dict(torch.load(actor_path, map_location=lambda storage, loc: storage))
for i in range(render_times):
cumulative_reward, episode_step = get_rewards_and_steps(env, actor, if_render=True)
print(f"|{i:4} cumulative_reward {cumulative_reward:9.3f} episode_step {episode_step:5.0f}")
class Evaluator:
def __init__(self, eval_env, eval_per_step: int = 1e4, eval_times: int = 8, cwd: str = '.'):
self.cwd = cwd
self.env_eval = eval_env
self.eval_step = 0
self.total_step = 0
self.start_time = time.time()
self.eval_times = eval_times # number of times that get episodic cumulative return
self.eval_per_step = eval_per_step # evaluate the agent per training steps
self.recorder = []
print("| Evaluator:"
"\n| `step`: Number of samples, or total training steps, or running times of `env.step()`."
"\n| `time`: Time spent from the start of training to this moment."
"\n| `avgR`: Average value of cumulative rewards, which is the sum of rewards in an episode."
"\n| `stdR`: Standard dev of cumulative rewards, which is the sum of rewards in an episode."
"\n| `avgS`: Average of steps in an episode."
"\n| `objC`: Objective of Critic network. Or call it loss function of critic network."
"\n| `objA`: Objective of Actor network. It is the average Q value of the critic network."
f"\n| {'step':>8} {'time':>8} | {'avgR':>8} {'stdR':>6} {'avgS':>6} | {'objC':>8} {'objA':>8}")
def evaluate_and_save(self, actor, horizon_len: int, logging_tuple: tuple):
self.total_step += horizon_len
if self.eval_step + self.eval_per_step > self.total_step:
return
self.eval_step = self.total_step
rewards_steps_ary = [get_rewards_and_steps(self.env_eval, actor) for _ in range(self.eval_times)]
rewards_steps_ary = np.array(rewards_steps_ary, dtype=np.float32)
avg_r = rewards_steps_ary[:, 0].mean() # average of cumulative rewards
std_r = rewards_steps_ary[:, 0].std() # std of cumulative rewards
avg_s = rewards_steps_ary[:, 1].mean() # average of steps in an episode
used_time = time.time() - self.start_time
self.recorder.append((self.total_step, used_time, avg_r))
save_path = f"{self.cwd}/actor_{self.total_step:012.0f}_{used_time:08.0f}_{avg_r:08.2f}.pth"
torch.save(actor.state_dict(), save_path)
print(f"| {self.total_step:8.2e} {used_time:8.0f} "
f"| {avg_r:8.2f} {std_r:6.2f} {avg_s:6.0f} "
f"| {logging_tuple[0]:8.2f} {logging_tuple[1]:8.2f}")
def close(self):
np.save(f"{self.cwd}/recorder.npy", np.array(self.recorder))
draw_learning_curve_using_recorder(self.cwd)
def get_rewards_and_steps(env, actor, if_render: bool = False) -> (float, int): # cumulative_rewards and episode_steps
if_discrete = env.if_discrete
device = next(actor.parameters()).device # net.parameters() is a Python generator.
state = env.reset()
episode_steps = 0
cumulative_returns = 0.0 # sum of rewards in an episode
for episode_steps in range(12345):
tensor_state = torch.as_tensor(state, dtype=torch.float32, device=device).unsqueeze(0)
tensor_action = actor(tensor_state).argmax(dim=1) if if_discrete else actor(tensor_state)
action = tensor_action.detach().cpu().numpy()[0] # not need detach(), because using torch.no_grad() outside
state, reward, done, _ = env.step(action)
cumulative_returns += reward
if if_render:
env.render()
time.sleep(0.02)
if done:
break
cumulative_returns = getattr(env, 'cumulative_returns', cumulative_returns)
return cumulative_returns, episode_steps + 1
def draw_learning_curve_using_recorder(cwd: str):
recorder = np.load(f"{cwd}/recorder.npy")
import matplotlib as mpl
mpl.use('Agg') # write before `import matplotlib.pyplot as plt`. `plt.savefig()` without a running X server
import matplotlib.pyplot as plt
x_axis = recorder[:, 0]
y_axis = recorder[:, 2]
plt.plot(x_axis, y_axis)
plt.xlabel('#samples (Steps)')
plt.ylabel('#Rewards (Score)')
plt.grid()
file_path = f"{cwd}/LearningCurve.jpg"
# plt.show() # if use `mpl.use('Agg')` to draw figures without GUI, then plt can't plt.show()
plt.savefig(file_path)
print(f"| Save learning curve in {file_path}")