add DDPG

codes/DDPG/agent.py

@@ -0,0 +1,93 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-09 20:25:52
+@LastEditor: John
+LastEditTime: 2021-03-17 20:43:25
+@Discription: 
+@Environment: python 3.7.7
+'''
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+from common.model import Actor, Critic
+from common.memory import ReplayBuffer
+
+
+class DDPG:
+    def __init__(self, n_states, n_actions, cfg):
+        self.device = cfg.device
+        self.critic = Critic(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
+        self.actor = Actor(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
+        self.target_critic = Critic(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
+        self.target_actor = Actor(n_states, n_actions, cfg.hidden_dim).to(cfg.device)
+
+        for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
+            target_param.data.copy_(param.data)
+        for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
+            target_param.data.copy_(param.data)
+
+        self.critic_optimizer = optim.Adam(
+            self.critic.parameters(),  lr=cfg.critic_lr)
+        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=cfg.actor_lr)
+        self.memory = ReplayBuffer(cfg.memory_capacity)
+        self.batch_size = cfg.batch_size
+        self.soft_tau = cfg.soft_tau
+        self.gamma = cfg.gamma
+
+    def choose_action(self, state):
+        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
+        action = self.actor(state)
+        # torch.detach()用于切断反向传播
+        return action.detach().cpu().numpy()[0, 0]
+
+    def update(self):
+        if len(self.memory) < self.batch_size:
+            return
+        state, action, reward, next_state, done = self.memory.sample(
+            self.batch_size)
+        # 将所有变量转为张量
+        state = torch.FloatTensor(state).to(self.device)
+        next_state = torch.FloatTensor(next_state).to(self.device)
+        action = torch.FloatTensor(action).to(self.device)
+        reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
+        done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
+        # 注意critic将(s_t,a)作为输入
+        policy_loss = self.critic(state, self.actor(state))
+        
+        policy_loss = -policy_loss.mean()
+
+        next_action = self.target_actor(next_state)
+        target_value = self.target_critic(next_state, next_action.detach())
+        expected_value = reward + (1.0 - done) * self.gamma * target_value
+        expected_value = torch.clamp(expected_value, -np.inf, np.inf)
+
+        value = self.critic(state, action)
+        value_loss = nn.MSELoss()(value, expected_value.detach())
+        
+        self.actor_optimizer.zero_grad()
+        policy_loss.backward()
+        self.actor_optimizer.step()
+
+        self.critic_optimizer.zero_grad()
+        value_loss.backward()
+        self.critic_optimizer.step()
+        for target_param, param in zip(self.target_critic.parameters(), self.critic.parameters()):
+            target_param.data.copy_(
+                target_param.data * (1.0 - self.soft_tau) +
+                param.data * self.soft_tau
+            )
+        for target_param, param in zip(self.target_actor.parameters(), self.actor.parameters()):
+            target_param.data.copy_(
+                target_param.data * (1.0 - self.soft_tau) +
+                param.data * self.soft_tau
+            )
+    def save(self,path):
+        torch.save(self.target_net.state_dict(), path+'DDPG_checkpoint.pth')
+
+    def load(self,path):
+        self.actor.load_state_dict(torch.load(path+'DDPG_checkpoint.pth'))