update projects

projects/codes/DDPG/ddpg.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-09 20:25:52
+@LastEditor: John
+LastEditTime: 2022-06-09 19:04:44
+@Discription: 
+@Environment: python 3.7.7
+'''
+import random
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+class ReplayBuffer:
+    def __init__(self, capacity):
+        self.capacity = capacity # 经验回放的容量
+        self.buffer = [] # 缓冲区
+        self.position = 0 
+    
+    def push(self, state, action, reward, next_state, done):
+        ''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition)
+        '''
+        if len(self.buffer) < self.capacity:
+            self.buffer.append(None)
+        self.buffer[self.position] = (state, action, reward, next_state, done)
+        self.position = (self.position + 1) % self.capacity 
+    
+    def sample(self, batch_size):
+        batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移
+        state, action, reward, next_state, done =  zip(*batch) # 解压成状态，动作等
+        return state, action, reward, next_state, done
+    
+    def __len__(self):
+        ''' 返回当前存储的量
+        '''
+        return len(self.buffer)
+class Actor(nn.Module):
+    def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3):
+        super(Actor, self).__init__()  
+        self.linear1 = nn.Linear(n_states, hidden_dim)
+        self.linear2 = nn.Linear(hidden_dim, hidden_dim)
+        self.linear3 = nn.Linear(hidden_dim, n_actions)
+        
+        self.linear3.weight.data.uniform_(-init_w, init_w)
+        self.linear3.bias.data.uniform_(-init_w, init_w)
+        
+    def forward(self, x):
+        x = F.relu(self.linear1(x))
+        x = F.relu(self.linear2(x))
+        x = torch.tanh(self.linear3(x))
+        return x
+class Critic(nn.Module):
+    def __init__(self, n_states, n_actions, hidden_dim, init_w=3e-3):
+        super(Critic, self).__init__()
+        
+        self.linear1 = nn.Linear(n_states + n_actions, hidden_dim)
+        self.linear2 = nn.Linear(hidden_dim, hidden_dim)
+        self.linear3 = nn.Linear(hidden_dim, 1)
+        # 随机初始化为较小的值
+        self.linear3.weight.data.uniform_(-init_w, init_w)
+        self.linear3.bias.data.uniform_(-init_w, init_w)
+        
+    def forward(self, state, action):
+        # 按维数1拼接
+        x = torch.cat([state, action], 1)
+        x = F.relu(self.linear1(x))
+        x = F.relu(self.linear2(x))
+        x = self.linear3(x)
+        return x
+class DDPG:
+    def __init__(self, n_states, n_actions, cfg):
+        self.device = torch.device(cfg.device)
+        self.critic = Critic(n_states, n_actions, cfg.hidden_dim).to(self.device)
+        self.actor = Actor(n_states, n_actions, cfg.hidden_dim).to(self.device)
+        self.target_critic = Critic(n_states, n_actions, cfg.hidden_dim).to(self.device)
+        self.target_actor = Actor(n_states, n_actions, cfg.hidden_dim).to(self.device)
+
+        # 复制参数到目标网络
+        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 # 软更新参数
+        self.gamma = cfg.gamma
+
+    def choose_action(self, state):
+        state = torch.FloatTensor(state).unsqueeze(0).to(self.device)
+        action = self.actor(state)
+        return action.detach().cpu().numpy()[0, 0]
+
+    def update(self):
+        if len(self.memory) < self.batch_size: # 当 memory 中不满足一个批量时，不更新策略
+            return
+        # 从经验回放中(replay memory)中随机采样一个批量的转移(transition)
+        state, action, reward, next_state, done = self.memory.sample(self.batch_size)
+        # 转变为张量
+        state = torch.FloatTensor(np.array(state)).to(self.device)
+        next_state = torch.FloatTensor(np.array(next_state)).to(self.device)
+        action = torch.FloatTensor(np.array(action)).to(self.device)
+        reward = torch.FloatTensor(reward).unsqueeze(1).to(self.device)
+        done = torch.FloatTensor(np.float32(done)).unsqueeze(1).to(self.device)
+       
+        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()
+        # 软更新
+        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.actor.state_dict(), path+'checkpoint.pt')
+
+    def load(self,path):
+        self.actor.load_state_dict(torch.load(path+'checkpoint.pt'))