Create PPO2.py

PPO/PPO2.py

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+
+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()