128 lines
6.2 KiB
Python
128 lines
6.2 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: 2021-03-13 14:56:23
|
|
@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 MLP2
|
|
class DQN:
|
|
def __init__(self, n_states, n_actions, cfg):
|
|
|
|
self.n_actions = n_actions # 总的动作个数
|
|
self.device = cfg.device # 设备，cpu或gpu等
|
|
self.gamma = cfg.gamma # 奖励的折扣因子
|
|
# e-greedy策略相关参数
|
|
self.sample_count = 0 # 用于epsilon的衰减计数
|
|
self.epsilon = 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 = MLP2(n_states, n_actions,hidden_dim=cfg.hidden_dim).to(self.device)
|
|
self.target_net = MLP2(n_states, n_actions,hidden_dim=cfg.hidden_dim).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=cfg.lr)
|
|
self.loss = 0
|
|
self.memory = ReplayBuffer(cfg.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.sample_count / self.epsilon_decay)
|
|
self.sample_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(): # 取消保存梯度
|
|
# 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)'''
|
|
'''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(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'))
|