#!/usr/bin/env python # coding=utf-8 ''' @Author: John @Email: johnjim0816@gmail.com @Date: 2020-06-12 00:50:49 @LastEditor: John LastEditTime: 2022-07-21 00:08:26 @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 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 MLP(nn.Module): def __init__(self, n_states,n_actions,hidden_dim=128): """ 初始化q网络，为全连接网络 n_states: 输入的特征数即环境的状态维度 n_actions: 输出的动作维度 """ super(MLP, self).__init__() self.fc1 = nn.Linear(n_states, hidden_dim) # 输入层 self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层 self.fc3 = nn.Linear(hidden_dim, n_actions) # 输出层 def forward(self, x): # 各层对应的激活函数 x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) return self.fc3(x) class DoubleDQN: def __init__(self, n_states, n_actions, cfg): self.n_actions = n_actions # 总的动作个数 self.device = torch.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(n_states, n_actions,hidden_dim=cfg.hidden_dim).to(self.device) self.target_net = MLP(n_states, n_actions,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 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: 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=) 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 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)