127 lines
5.4 KiB
Python
127 lines
5.4 KiB
Python
#!/usr/bin/env python
|
||
# coding=utf-8
|
||
'''
|
||
@Author: John
|
||
@Email: johnjim0816@gmail.com
|
||
@Date: 2020-06-12 00:50:49
|
||
@LastEditor: John
|
||
LastEditTime: 2022-07-20 23:57:16
|
||
@Discription:
|
||
@Environment: python 3.7.7
|
||
'''
|
||
'''off-policy
|
||
'''
|
||
|
||
import torch
|
||
import torch.nn as nn
|
||
import torch.nn.functional as F
|
||
import torch.optim as optim
|
||
import random
|
||
import math
|
||
import numpy as np
|
||
|
||
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 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 DQN:
|
||
def __init__(self, n_states,n_actions,cfg):
|
||
|
||
self.n_actions = n_actions
|
||
self.device = torch.device(cfg.device) # cpu or cuda
|
||
self.gamma = cfg.gamma # 奖励的折扣因子
|
||
# e-greedy策略相关参数
|
||
self.frame_idx = 0 # 用于epsilon的衰减计数
|
||
self.epsilon = lambda frame_idx: cfg.epsilon_end + \
|
||
(cfg.epsilon_start - cfg.epsilon_end) * \
|
||
math.exp(-1. * frame_idx / cfg.epsilon_decay)
|
||
self.batch_size = cfg.batch_size
|
||
self.policy_net = MLP(n_states,n_actions).to(self.device)
|
||
self.target_net = MLP(n_states,n_actions).to(self.device)
|
||
for target_param, param in zip(self.target_net.parameters(),self.policy_net.parameters()): # 复制参数到目标网路targe_net
|
||
target_param.data.copy_(param.data)
|
||
self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器
|
||
self.memory = ReplayBuffer(cfg.memory_capacity) # 经验回放
|
||
|
||
def choose_action(self, state):
|
||
''' 选择动作
|
||
'''
|
||
self.frame_idx += 1
|
||
if random.random() > self.epsilon(self.frame_idx):
|
||
with torch.no_grad():
|
||
state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(dim=0)
|
||
q_values = self.policy_net(state)
|
||
action = q_values.max(1)[1].item() # 选择Q值最大的动作
|
||
else:
|
||
action = random.randrange(self.n_actions)
|
||
return action
|
||
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)
|
||
# print('updating')
|
||
|
||
state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(
|
||
self.batch_size)
|
||
state_batch = torch.tensor(np.array(state_batch), device=self.device, dtype=torch.float)
|
||
action_batch = torch.tensor(action_batch, device=self.device).unsqueeze(1)
|
||
reward_batch = torch.tensor(reward_batch, device=self.device, dtype=torch.float)
|
||
next_state_batch = torch.tensor(np.array(next_state_batch), device=self.device, dtype=torch.float)
|
||
done_batch = torch.tensor(np.float32(done_batch), device=self.device)
|
||
q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch) # 计算当前状态(s_t,a)对应的Q(s_t, a)
|
||
next_q_values = self.target_net(next_state_batch).max(1)[0].detach() # 计算下一时刻的状态(s_t_,a)对应的Q值
|
||
# 计算期望的Q值，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_q_values * (1-done_batch)
|
||
loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) # 计算均方根损失
|
||
# 优化更新模型
|
||
self.optimizer.zero_grad()
|
||
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()
|
||
|
||
def save(self, path):
|
||
torch.save(self.target_net.state_dict(), path+'dqn_checkpoint.pth')
|
||
|
||
def load(self, path):
|
||
self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth'))
|
||
for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
|
||
param.data.copy_(target_param.data)
|