128 lines
6.2 KiB
Python
128 lines
6.2 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: 2021-03-13 14:56:23
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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 random
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import math
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import numpy as np
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from common.memory import ReplayBuffer
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from common.model import MLP
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class DQN:
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def __init__(self, state_dim, action_dim, cfg):
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self.action_dim = action_dim # 总的动作个数
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self.device = cfg.device # 设备，cpu或gpu等
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self.gamma = cfg.gamma # 奖励的折扣因子
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# e-greedy策略相关参数
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self.sample_count = 0 # 用于epsilon的衰减计数
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self.epsilon = 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(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
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self.target_net = MLP(state_dim, action_dim,hidden_dim=cfg.hidden_dim).to(self.device)
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# target_net的初始模型参数完全复制policy_net
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self.target_net.load_state_dict(self.policy_net.state_dict())
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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, train=True):
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'''选择动作
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'''
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if train:
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self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
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math.exp(-1. * self.sample_count / self.epsilon_decay)
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self.sample_count += 1
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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.action_dim)
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return action
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else:
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with torch.no_grad(): # 取消保存梯度
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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='cpu', dtype=torch.float32) # 如tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
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q_value = self.target_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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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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'''转为张量
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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]])'''
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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).unsqueeze(1) # 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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'''torch.gather:对于a=torch.Tensor([[1,2],[3,4]]),那么a.gather(1,torch.Tensor([[0],[1]]))=torch.Tensor([[1],[3]])'''
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q_values = self.policy_net(state_batch).gather(
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dim=1, index=action_batch) # 等价于self.forward
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# 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
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next_state_values = 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 expected_q_value
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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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expected_q_values = reward_batch + self.gamma * \
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next_state_values * (1-done_batch[0])
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# self.loss = F.smooth_l1_loss(q_values,expected_q_values.unsqueeze(1)) # 计算 Huber loss
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self.loss = nn.MSELoss()(q_values, expected_q_values.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+'dqn_checkpoint.pth')
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def load(self,path):
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self.target_net.load_state_dict(torch.load(path+'dqn_checkpoint.pth'))
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