update codes

codes/DoubleDQN/agent.py

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+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:50:49
+@LastEditor: John
+LastEditTime: 2021-05-04 22:28:06
+@Discription: 
+@Environment: python 3.7.7
+'''
+'''off-policy
+'''
+
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import random
+import math
+import numpy as np
+from common.memory import ReplayBuffer
+from common.model import MLP
+class DoubleDQN:
+    def __init__(self, state_dim, action_dim, cfg):
+        
+        self.action_dim = action_dim  # 总的动作个数
+        self.device = cfg.device  # 设备，cpu或gpu等
+        self.gamma = cfg.gamma
+        # e-greedy策略相关参数
+        self.actions_count = 0
+        self.epsilon_start = cfg.epsilon_start
+        self.epsilon_end = cfg.epsilon_end
+        self.epsilon_decay = cfg.epsilon_decay
+        self.batch_size = cfg.batch_size
+        self.policy_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
+        self.target_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
+        # target_net copy from policy_net
+        for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
+            target_param.data.copy_(param.data)
+        # self.target_net.eval()  # 不启用 BatchNormalization 和 Dropout
+        # 可查parameters()与state_dict()的区别，前者require_grad=True
+        self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
+        self.loss = 0
+        self.memory = ReplayBuffer(cfg.memory_capacity)
+    def predict(self,state):
+        with torch.no_grad():
+                # 先转为张量便于丢给神经网络,state元素数据原本为float64
+                # 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
+                state = torch.tensor(
+                    [state], device=self.device, dtype=torch.float32)
+                # 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
+                q_value = self.policy_net(state)
+                # tensor.max(1)返回每行的最大值以及对应的下标，
+                # 如torch.return_types.max(values=tensor([10.3587]),indices=tensor([0]))
+                # 所以tensor.max(1)[1]返回最大值对应的下标，即action
+                action = q_value.max(1)[1].item()  
+        return action
+    def choose_action(self, state):
+        '''选择动作
+        '''
+        self.actions_count += 1
+        self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
+            math.exp(-1. * self.actions_count / self.epsilon_decay)
+        if random.random() > self.epsilon:
+            action  = self.predict(state)
+        else:
+            action = random.randrange(self.action_dim)
+        return action
+    def update(self):
+
+        if len(self.memory) < self.batch_size:
+            return
+        # 从memory中随机采样transition
+        state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
+            self.batch_size)
+        # convert to tensor
+        state_batch = torch.tensor(
+            state_batch, device=self.device, dtype=torch.float)
+        action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(
+            1)  # 例如tensor([[1],...,[0]])
+        reward_batch = torch.tensor(
+            reward_batch, device=self.device, dtype=torch.float)  # tensor([1., 1.,...,1])
+        next_state_batch = torch.tensor(
+            next_state_batch, device=self.device, dtype=torch.float)
+        
+        done_batch = torch.tensor(np.float32(
+            done_batch), device=self.device)  # 将bool转为float然后转为张量
+        # 计算当前(s_t,a)对应的Q(s_t, a)
+        q_values = self.policy_net(state_batch) 
+        next_q_values = self.policy_net(next_state_batch)
+        # 代入当前选择的action，得到Q(s_t|a=a_t)
+        q_value = q_values.gather(dim=1, index=action_batch)
+        '''以下是Nature DQN的q_target计算方式
+        # 计算所有next states的Q'(s_{t+1})的最大值，Q'为目标网络的q函数
+        next_q_state_value = self.target_net(
+            next_state_batch).max(1)[0].detach()  # 比如tensor([ 0.0060, -0.0171,...,])
+        # 计算 q_target
+        # 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
+        q_target = reward_batch + self.gamma * next_q_state_value * (1-done_batch[0])
+        '''
+        '''以下是Double DQN q_target计算方式，与NatureDQN稍有不同'''
+        next_target_values = self.target_net(
+            next_state_batch)
+        # 选出Q(s_t‘, a)对应的action，代入到next_target_values获得target net对应的next_q_value，即Q’(s_t|a=argmax Q(s_t‘, a))
+        next_target_q_value = next_target_values.gather(1, torch.max(next_q_values, 1)[1].unsqueeze(1)).squeeze(1)
+        q_target = reward_batch + self.gamma * next_target_q_value * (1-done_batch)
+        self.loss = nn.MSELoss()(q_value, q_target.unsqueeze(1))  # 计算 均方误差loss
+        # 优化模型
+        self.optimizer.zero_grad()  # zero_grad清除上一步所有旧的gradients from the last step
+        # loss.backward()使用backpropagation计算loss相对于所有parameters(需要gradients)的微分
+        self.loss.backward()
+        for param in self.policy_net.parameters():  # clip防止梯度爆炸
+            param.grad.data.clamp_(-1, 1)
+        self.optimizer.step()  # 更新模型
+
+    def save(self,path):
+        torch.save(self.target_net.state_dict(), path+'checkpoint.pth')
+
+    def load(self,path):
+        self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))  
+        for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
+            param.data.copy_(target_param.data)