diff --git a/PPO/PPO2.py b/PPO/PPO2.py new file mode 100644 index 0000000..ad3f9d5 --- /dev/null +++ b/PPO/PPO2.py @@ -0,0 +1,188 @@ + +import argparse +import pickle +from collections import namedtuple + +import os +import numpy as np +import matplotlib.pyplot as plt + +import gym +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +from torch.distributions import Normal +from torch.utils.data.sampler import BatchSampler, SubsetRandomSampler + +# Parameters +parser = argparse.ArgumentParser(description='Solve the Pendulum-v0 with PPO') +parser.add_argument( + '--gamma', type=float, default=0.9, metavar='G', help='discount factor (default: 0.9)') +parser.add_argument('--seed', type=int, default=0, metavar='N', help='random seed (default: 0)') +parser.add_argument('--render', action='store_true', default=True, help='render the environment') +parser.add_argument( + '--log-interval', + type=int, + default=10, + metavar='N', + help='interval between training status logs (default: 10)') +args = parser.parse_args() + +env = gym.make('Pendulum-v0').unwrapped +num_state = env.observation_space.shape[0] +num_action = env.action_space.shape[0] +torch.manual_seed(args.seed) +env.seed(args.seed) + +Transition = namedtuple('Transition',['state', 'aciton', 'reward', 'a_log_prob', 'next_state']) +TrainRecord = namedtuple('TrainRecord',['episode', 'reward']) + +class Actor(nn.Module): + def __init__(self): + super(Actor, self).__init__() + self.fc1 = nn.Linear(num_state, 64) + self.fc2 = nn.Linear(64,8) + self.mu_head = nn.Linear(8, 1) + self.sigma_head = nn.Linear(8, 1) + + def forward(self, x): + x = F.leaky_relu(self.fc1(x)) + x = F.leaky_relu(self.fc2(x)) + + mu = self.mu_head(x) + sigma = self.sigma_head(x) + + return mu, sigma + +class Critic(nn.Module): + def __init__(self): + super(Critic, self).__init__() + self.fc1 = nn.Linear(num_state, 64) + self.fc2 = nn.Linear(64, 8) + self.state_value= nn.Linear(8, 1) + + def forward(self, x): + x = F.leaky_relu(self.fc1(x)) + x = F.leaky_relu(self.fc2(x)) + value = self.state_value(x) + return value + +class PPO(): + clip_param = 0.2 + max_grad_norm = 0.5 + ppo_epoch = 10 + buffer_capacity = 1000 + batch_size = 8 + + def __init__(self): + super(PPO, self).__init__() + self.actor_net = Actor().float() + self.critic_net = Critic().float() + self.buffer = [] + self.counter = 0 + self.training_step = 0 + + self.actor_optimizer = optim.Adam(self.actor_net.parameters(), 1e-3) + self.critic_net_optimizer = optim.Adam(self.critic_net.parameters(), 4e-3) + if not os.path.exists('../param'): + os.makedirs('../param/net_param') + os.makedirs('../param/img') + + def select_action(self, state): + state = torch.from_numpy(state).float().unsqueeze(0) + with torch.no_grad(): + mu, sigma = self.actor_net(state) + dist = Normal(mu, sigma) + action = dist.sample() + action_log_prob = dist.log_prob(action) + action = action.clamp(-2, 2) + return action.item(), action_log_prob.item() + + + def get_value(self, state): + state = torch.from_numpy(state) + with torch.no_grad(): + value = self.critic_net(state) + return value.item() + + def save_param(self): + torch.save(self.actor_net.state_dict(), '../param/net_param/actor_net'+str(time.time())[:10],+'.pkl') + torch.save(self.critic_net.state_dict(), '../param/net_param/critic_net'+str(time.time())[:10],+'.pkl') + + def store_transition(self, transition): + self.buffer.append(transition) + self.counter+=1 + return counter % self.buffer_capacity == 0 + + def update(self): + self.training_step +=1 + + state = torch.tensor([t.state for t in self.buffer ], dtype=torch.float) + action = torch.tensor([t.action for t in self.buffer], dtype=torch.float).view(-1, 1) + reward = torch.tensor([t.reward for t in self.buffer], dtype=torch.float).view(-1, 1) + next_state = torch.tensor([t.next_state for t in self.buffer], dtype=torch.float) + old_action_log_prob = torch.tensor([t.a_log_prob for t in self.buffer], dtype=torch.float).view(-1, 1) + + reward = (reward - reward.mean())/(reward.std() + 1e-10) + with torch.no_grad(): + target_v = reward + args.gamma * self.critic_net(next_state) + + advantage = (target_v - self.critic_net(state)).detach() + for _ in range(self.ppo_epoch): # iteration ppo_epoch + for index in BatchSampler(SubsetRandomSampler(range(self.buffer_capacity), self.batch_size, True)): + # epoch iteration, PPO core!!! + mu, sigma = self.actor_net(state[index]) + n = Normal(mu, sigma) + action_log_prob = n.log_prob(action[index]) + ratio = torch.exp(action_log_prob - old_action_log_prob) + + L1 = ratio * advantage[index] + L2 = torch.clamp(ratio, 1-self.clip_param, 1+self.clip_param) * advantage[index] + action_loss = -torch.min(L1, L2).mean() # MAX->MIN desent + self.actor_optimizer.zero_grad() + action_loss.backward() + nn.utils.clip_grad_norm_(self.actor_net.parameters(), self.max_grad_norm) + self.actor_optimizer.step() + + value_loss = F.smooth_l1_loss(self.critic_net(state[index]), target_v[index]) + self.critic_net_optimizer.zero_grad() + value_loss.backward() + nn.utils.clip_grad_norm_(self.critic_net.parameters(), self.max_grad_norm) + self.critic_net_optimizer.step() + + del self.buffer[:] + +def main(): + + agent = PPO() + + training_records = [] + running_reward = -1000 + + for i_epoch in range(1000): + score = 0 + state = env.reset() + if args.render: env.render() + for t in range(200): + action, action_log_prob = agent.select_action(state) + next_state, reward, done, info = env.step(action) + trans = Transition(state, action, reward, action_log_prob, next_state) + if args.render: env.render() + if agent.store_transition(trans): + agent.update() + score += reward + state = next_state + + running_reward = running_reward * 0.9 + score * 0.1 + training_records.append(TrainingRecord(i_epoch, running_reward)) + if i_epoch % 10 ==0: + print("Epoch {}, Moving average score is: {:.2f} ".format(i_epoch, running_reward)) + if running_reward > -200: + print("Solved! Moving average score is now {}!".format(running_reward)) + env.close() + agent.save_param() + break + +if __name__ == '__main__': + main()