161 lines
7.2 KiB
Python
161 lines
7.2 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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'''
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@Author: John
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@Email: johnjim0816@gmail.com
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@Date: 2020-06-12 00:50:49
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@LastEditor: John
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LastEditTime: 2022-07-21 00:08:26
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@Discription:
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@Environment: python 3.7.7
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'''
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'''off-policy
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'''
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import torch
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import torch.nn as nn
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import torch.optim as optim
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import torch.nn.functional as F
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import random
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import math
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import numpy as np
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class ReplayBuffer:
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def __init__(self, capacity):
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self.capacity = capacity # 经验回放的容量
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self.buffer = [] # 缓冲区
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self.position = 0
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def push(self, state, action, reward, next_state, done):
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''' 缓冲区是一个队列，容量超出时去掉开始存入的转移(transition)
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'''
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if len(self.buffer) < self.capacity:
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self.buffer.append(None)
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self.buffer[self.position] = (state, action, reward, next_state, done)
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self.position = (self.position + 1) % self.capacity
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def sample(self, batch_size):
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batch = random.sample(self.buffer, batch_size) # 随机采出小批量转移
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state, action, reward, next_state, done = zip(*batch) # 解压成状态，动作等
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return state, action, reward, next_state, done
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def __len__(self):
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''' 返回当前存储的量
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'''
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return len(self.buffer)
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class MLP(nn.Module):
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def __init__(self, n_states,n_actions,hidden_dim=128):
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""" 初始化q网络，为全连接网络
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n_states: 输入的特征数即环境的状态维度
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n_actions: 输出的动作维度
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"""
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super(MLP, self).__init__()
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self.fc1 = nn.Linear(n_states, hidden_dim) # 输入层
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self.fc2 = nn.Linear(hidden_dim,hidden_dim) # 隐藏层
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self.fc3 = nn.Linear(hidden_dim, n_actions) # 输出层
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def forward(self, x):
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# 各层对应的激活函数
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x = F.relu(self.fc1(x))
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x = F.relu(self.fc2(x))
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return self.fc3(x)
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class DoubleDQN:
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def __init__(self, n_states, n_actions, cfg):
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self.n_actions = n_actions # 总的动作个数
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self.device = torch.device(cfg.device) # 设备，cpu或gpu等
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self.gamma = cfg.gamma
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# e-greedy策略相关参数
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self.actions_count = 0
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self.epsilon_start = cfg.epsilon_start
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self.epsilon_end = cfg.epsilon_end
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self.epsilon_decay = cfg.epsilon_decay
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self.batch_size = cfg.batch_size
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self.policy_net = MLP(n_states, n_actions,hidden_dim=cfg.hidden_dim).to(self.device)
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self.target_net = MLP(n_states, n_actions,hidden_dim=cfg.hidden_dim).to(self.device)
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# target_net copy from policy_net
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for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
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target_param.data.copy_(param.data)
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# self.target_net.eval() # 不启用 BatchNormalization 和 Dropout
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# 可查parameters()与state_dict()的区别，前者require_grad=True
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self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr)
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self.loss = 0
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self.memory = ReplayBuffer(cfg.memory_capacity)
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def choose_action(self, state):
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'''选择动作
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'''
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self.actions_count += 1
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self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * math.exp(-1. * self.actions_count / self.epsilon_decay)
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if random.random() > self.epsilon:
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with torch.no_grad():
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# 先转为张量便于丢给神经网络,state元素数据原本为float64
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# 注意state=torch.tensor(state).unsqueeze(0)跟state=torch.tensor([state])等价
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state = torch.tensor(
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[state], device=self.device, dtype=torch.float32)
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# 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
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q_value = self.policy_net(state)
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# tensor.max(1)返回每行的最大值以及对应的下标，
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# 如torch.return_types.max(values=tensor([10.3587]),indices=tensor([0]))
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# 所以tensor.max(1)[1]返回最大值对应的下标，即action
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action = q_value.max(1)[1].item()
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else:
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action = random.randrange(self.n_actions)
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return action
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def update(self):
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if len(self.memory) < self.batch_size:
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return
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# 从memory中随机采样transition
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state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
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self.batch_size)
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# convert to tensor
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state_batch = torch.tensor(
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state_batch, device=self.device, dtype=torch.float)
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action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(
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1) # 例如tensor([[1],...,[0]])
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reward_batch = torch.tensor(
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reward_batch, device=self.device, dtype=torch.float) # tensor([1., 1.,...,1])
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next_state_batch = torch.tensor(
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next_state_batch, device=self.device, dtype=torch.float)
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done_batch = torch.tensor(np.float32(
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done_batch), device=self.device) # 将bool转为float然后转为张量
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# 计算当前(s_t,a)对应的Q(s_t, a)
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q_values = self.policy_net(state_batch)
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next_q_values = self.policy_net(next_state_batch)
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# 代入当前选择的action，得到Q(s_t|a=a_t)
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q_value = q_values.gather(dim=1, index=action_batch)
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'''以下是Nature DQN的q_target计算方式
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# 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
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next_q_state_value = self.target_net(
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next_state_batch).max(1)[0].detach() # 比如tensor([ 0.0060, -0.0171,...,])
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# 计算 q_target
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# 对于终止状态，此时done_batch[0]=1, 对应的expected_q_value等于reward
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q_target = reward_batch + self.gamma * next_q_state_value * (1-done_batch[0])
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'''
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'''以下是Double DQN q_target计算方式，与NatureDQN稍有不同'''
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next_target_values = self.target_net(
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next_state_batch)
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# 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))
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next_target_q_value = next_target_values.gather(1, torch.max(next_q_values, 1)[1].unsqueeze(1)).squeeze(1)
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q_target = reward_batch + self.gamma * next_target_q_value * (1-done_batch)
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self.loss = nn.MSELoss()(q_value, q_target.unsqueeze(1)) # 计算 均方误差loss
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# 优化模型
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self.optimizer.zero_grad() # zero_grad清除上一步所有旧的gradients from the last step
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# loss.backward()使用backpropagation计算loss相对于所有parameters(需要gradients)的微分
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self.loss.backward()
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for param in self.policy_net.parameters(): # clip防止梯度爆炸
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param.grad.data.clamp_(-1, 1)
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self.optimizer.step() # 更新模型
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def save(self,path):
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torch.save(self.target_net.state_dict(), path+'checkpoint.pth')
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def load(self,path):
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self.target_net.load_state_dict(torch.load(path+'checkpoint.pth'))
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for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
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param.data.copy_(target_param.data)
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