update DQN

codes/dqn/agent.py

@@ -0,0 +1,128 @@
+#!/usr/bin/env python
+# coding=utf-8
+'''
+@Author: John
+@Email: johnjim0816@gmail.com
+@Date: 2020-06-12 00:50:49
+@LastEditor: John
+LastEditTime: 2020-10-15 21:56:21
+@Discription: 
+@Environment: python 3.7.7
+'''
+'''off-policy
+'''
+
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torch.nn.functional as F
+import random
+import math
+import numpy as np
+from memory import ReplayBuffer
+from model import FCN
+class DQN:
+    def __init__(self, n_states, n_actions, gamma=0.99, epsilon_start=0.9, epsilon_end=0.05, epsilon_decay=200, memory_capacity=10000, policy_lr=0.01, batch_size=128, device="cpu"):
+        self.actions_count = 0
+        self.n_actions = n_actions  # 总的动作个数
+        self.device = device  # 设备，cpu或gpu等
+        self.gamma = gamma
+        # e-greedy策略相关参数
+        self.epsilon = 0
+        self.epsilon_start = epsilon_start
+        self.epsilon_end = epsilon_end
+        self.epsilon_decay = epsilon_decay
+        self.batch_size = batch_size
+        self.policy_net = FCN(n_states, n_actions).to(self.device)
+        self.target_net = FCN(n_states, n_actions).to(self.device)
+        # target_net的初始模型参数完全复制policy_net
+        self.target_net.load_state_dict(self.policy_net.state_dict())
+        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=policy_lr)
+        self.loss = 0
+        self.memory = ReplayBuffer(memory_capacity)
+
+    def choose_action(self, state, train=True):
+        '''选择动作
+        '''
+        if train:
+            self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
+                math.exp(-1. * self.actions_count / self.epsilon_decay)
+            self.actions_count += 1
+            if random.random() > self.epsilon:
+                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()  
+            else:
+                action = random.randrange(self.n_actions)
+            return action
+        else: 
+            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='cpu', dtype=torch.float32)
+                    # 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
+                    q_value = self.target_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 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)
+        # 转为张量
+        # 例如tensor([[-4.5543e-02, -2.3910e-01,  1.8344e-02,  2.3158e-01],...,[-1.8615e-02, -2.3921e-01, -1.1791e-02,  2.3400e-01]])
+        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).unsqueeze(1)  # 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)
+        # 关于torch.gather,对于a=torch.Tensor([[1,2],[3,4]])
+        # 那么a.gather(1,torch.Tensor([[0],[1]]))=torch.Tensor([[1],[3]])
+        q_values = self.policy_net(state_batch).gather(
+            dim=1, index=action_batch)  # 等价于self.forward
+        # 计算所有next states的V(s_{t+1})，即通过target_net中选取reward最大的对应states
+        next_state_values = self.target_net(
+            next_state_batch).max(1)[0].detach()  # 比如tensor([ 0.0060, -0.0171,...,])
+        # 计算 expected_q_value
+        # 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
+        expected_q_values = reward_batch + self.gamma * \
+            next_state_values * (1-done_batch[0])
+        # self.loss = F.smooth_l1_loss(q_values,expected_q_values.unsqueeze(1)) # 计算 Huber loss
+        self.loss = nn.MSELoss()(q_values, expected_q_values.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_model(self,path):
+        torch.save(self.target_net.state_dict(), path)
+
+    def load_model(self,path):
+        self.target_net.load_state_dict(torch.load(path))