99 lines
4.8 KiB
Python
99 lines
4.8 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: 2020-06-14 13:56:45
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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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from memory import ReplayBuffer
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from model import FCN
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class DQN:
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def __init__(self, n_states, n_actions, gamma=0.99, epsilon_start=0.9, epsilon_end=0.05, epsilon_decay=200, memory_capacity=10000, policy_lr=0.01,batch_size=128, device="cpu"):
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self.actions_count = 0
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self.n_actions = n_actions
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self.device = device
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self.gamma = gamma
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self.epsilon = 0
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self.epsilon_start = epsilon_start
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self.epsilon_end = epsilon_end
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self.epsilon_decay = epsilon_decay
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self.batch_size = batch_size
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self.policy_net = FCN(n_states,n_actions).to(self.device)
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self.target_net = FCN(n_states,n_actions).to(self.device)
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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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self.optimizer = optim.Adam(self.policy_net.parameters(),lr=policy_lr)
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self.loss = 0
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self.memory = ReplayBuffer(memory_capacity)
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def select_action(self,state):
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'''选择工作
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Args:
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state [array]: 状态
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Returns:
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[array]: 动作
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'''
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self.epsilon = self.epsilon_end + (self.epsilon_start - self.epsilon_end) * \
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math.exp(-1. * self.actions_count / self.epsilon_decay)
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self.actions_count += 1
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if random.random() > self.epsilon:
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with torch.no_grad():
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state = torch.tensor([state],device=self.device,dtype=torch.float32) # 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
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q_value = self.policy_net(state) # tensor([[-0.0798, -0.0079]], grad_fn=<AddmmBackward>)
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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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state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.memory.sample(self.batch_size)
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state_batch = torch.tensor(state_batch,device=self.device,dtype=torch.float) # 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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action_batch = torch.tensor(action_batch,device=self.device).unsqueeze(1) # 例如tensor([[1],...,[0]])
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reward_batch = torch.tensor(reward_batch,device=self.device,dtype=torch.float) # tensor([1., 1.,...,1])
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next_state_batch = torch.tensor(next_state_batch,device=self.device,dtype=torch.float)
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done_batch = torch.tensor(np.float32(done_batch),device=self.device).unsqueeze(1) # 将bool转为float然后转为张量
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# Compute Q(s_t, a) - the model computes Q(s_t), then we select the
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# columns of actions taken. These are the actions which would've been taken
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# for each batch state according to policy_net
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q_values = self.policy_net(state_batch).gather(1, action_batch) # 等价于self.forward
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# Compute V(s_{t+1}) for all next states.
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# Expected values of actions for non_final_next_states are computed based
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# on the "older" target_net; selecting their best reward with max(1)[0].
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# This is merged based on the mask, such that we'll have either the expected
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# state value or 0 in case the state was final.
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next_state_values = self.target_net(
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next_state_batch).max(1)[0].detach() # tensor([ 0.0060, -0.0171,...,])
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# Compute the expected Q values
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expected_q_values = reward_batch + self.gamma * next_state_values * (1-done_batch[0])
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# Compute Huber loss
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# self.loss = nn.MSELoss(q_values, expected_q_values.unsqueeze(1))
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self.loss = nn.MSELoss()(q_values,expected_q_values.unsqueeze(1))
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# Optimize the model
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self.optimizer.zero_grad() # zero_grad clears old gradients from the last step (otherwise you’d just accumulate the gradients from all loss.backward() calls).
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self.loss.backward() # loss.backward() computes the derivative of the loss w.r.t. the parameters (or anything requiring gradients) using backpropagation.
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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() # causes the optimizer to take a step based on the gradients of the parameters.
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