sdt_ppo_gae_discrete.py

import gym
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.distributions import Categorical
import argparse
import matplotlib.pyplot as plt
import numpy as np
from SDT.sdt_train import learner_args, device
from SDT.SDT import SDT 

#Hyperparameters
learning_rate = 0.0005
gamma         = 0.98
lmbda         = 0.95
eps_clip      = 0.1
K_epoch       = 3
# T_horizon     = 20
model_path = './model/sdt_ppo_discrete_lunarlandar'

class PPO(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(PPO, self).__init__()
        self.data = []
        hidden_dim=128
        self.fc1   = nn.Linear(state_dim,hidden_dim)
        self.fc_v  = nn.Linear(hidden_dim,1)

        self.sdt = SDT(learner_args).to(device)
        self.pi = lambda x: self.sdt.forward(x, LogProb=False)[1]

        self.optimizer = optim.Adam(list(self.parameters())+list(self.sdt.parameters()), lr=learning_rate)

    def v(self, x):
        if isinstance(x, (np.ndarray, np.generic) ):
            x = torch.tensor(x)
        x = F.relu(self.fc1(x))
        v = self.fc_v(x)
        return v
      
    def put_data(self, transition):
        self.data.append(transition)
        
    def make_batch(self):
        s_lst, a_lst, r_lst, s_prime_lst, prob_a_lst, done_lst = [], [], [], [], [], []
        for transition in self.data:
            s, a, r, s_prime, prob_a, done = transition
            
            s_lst.append(s)
            a_lst.append([a])
            r_lst.append([r])
            s_prime_lst.append(s_prime)
            prob_a_lst.append([prob_a])
            done_mask = 0 if done else 1
            done_lst.append([done_mask])
            
        s,a,r,s_prime,done_mask, prob_a = torch.tensor(s_lst, dtype=torch.float).to(device), torch.tensor(a_lst).to(device), \
                                          torch.tensor(r_lst).to(device), torch.tensor(s_prime_lst, dtype=torch.float).to(device), \
                                          torch.tensor(done_lst, dtype=torch.float).to(device), torch.tensor(prob_a_lst).to(device)
        self.data = []
        return s, a, r, s_prime, done_mask, prob_a
        
    def train_net(self):
        s, a, r, s_prime, done_mask, prob_a = self.make_batch()

        for i in range(K_epoch):
            td_target = r + gamma * self.v(s_prime) * done_mask
            delta = td_target - self.v(s)
            delta = delta.detach()

            advantage_lst = []
            advantage = 0.0
            for delta_t in torch.flip(delta, [0]):
                advantage = gamma * lmbda * advantage + delta_t[0]
                advantage_lst.append([advantage])
            advantage_lst.reverse()
            advantage = torch.tensor(advantage_lst, dtype=torch.float).to(device)

            pi = self.pi(s)
            pi_a = pi.gather(1,a)
            ratio = torch.exp(torch.log(pi_a) - torch.log(prob_a))  # a/b == exp(log(a)-log(b))
            surr1 = ratio * advantage
            surr2 = torch.clamp(ratio, 1-eps_clip, 1+eps_clip) * advantage
            loss = -torch.min(surr1, surr2) + F.smooth_l1_loss(self.v(s) , td_target.detach())

            self.optimizer.zero_grad()
            loss.mean().backward()
            self.optimizer.step()

    def choose_action(self, s):
        prob = self.pi(torch.from_numpy(s).unsqueeze(0).float().to(device)).squeeze()
        m = Categorical(prob)
        a = m.sample().item()
        return a, prob

    def load_model(self, ):
        self.load_state_dict(torch.load(model_path))


def plot(rewards):
    # clear_output(True)
    plt.figure(figsize=(10,5))
    plt.plot(rewards)
    plt.savefig('sdt_ppo_discrete_lunarlandar.png')
    # plt.show()
    plt.clf()  
    plt.close()

def run(train=False, test=False):
    # env = gym.make('CartPole-v1')
    env = gym.make('LunarLander-v2')
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n  # discrete
    print(state_dim, action_dim)
    model = PPO(state_dim, action_dim).to(device)
    print_interval = 20
    if test:
        model.load_model()
    rewards_list=[]
    for n_epi in range(10000):
        s = env.reset()
        done = False
        reward = 0.0
        step=0
        while not done:
            # for t in range(T_horizon):
            a, prob = model.choose_action(s)
            s_prime, r, done, info = env.step(a)
            if test:
                env.render()
            model.put_data((s, a, r/100.0, s_prime, prob[a].item(), done))
            # model.put_data((s, a, r, s_prime, prob[a].item(), done))

            s = s_prime

            reward += r
            step+=1
            if done:
                break
        if train:
            model.train_net()
        rewards_list.append(reward)
        if train:   
            if n_epi%print_interval==0 and n_epi!=0:
                # plot(rewards_list)
                np.save('./log/sdt_ppo_discrete', rewards_list)
                torch.save(model.state_dict(), model_path)
                print("# of episode :{}, reward : {:.1f}, episode length: {}".format(n_epi, reward, step))
        else:
            print("# of episode :{}, reward : {:.1f}, episode length: {}".format(n_epi, reward, step))

    env.close()

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='Train or test neural net motor controller.')
    parser.add_argument('--train', dest='train', action='store_true', default=False)
    parser.add_argument('--test', dest='test', action='store_true', default=False)
    args = parser.parse_args()

    if args.train:
        run(train=True, test=False)
    if args.test:
        run(train=False, test=True)