154 lines
6.8 KiB
Python
154 lines
6.8 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: 2021-03-24 22:18:18
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LastEditor: John
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LastEditTime: 2021-05-04 22:39:34
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Discription:
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Environment:
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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 numpy as np
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import random,math
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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, input_dim,output_dim,hidden_dim=128):
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""" 初始化q网络，为全连接网络
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input_dim: 输入的特征数即环境的状态维度
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output_dim: 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(input_dim, 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, output_dim) # 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 HierarchicalDQN:
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def __init__(self,n_states,n_actions,cfg):
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self.n_states = n_states
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self.n_actions = n_actions
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self.gamma = cfg.gamma
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self.device = cfg.device
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self.batch_size = cfg.batch_size
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self.frame_idx = 0 # 用于epsilon的衰减计数
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self.epsilon = lambda frame_idx: cfg.epsilon_end + (cfg.epsilon_start - cfg.epsilon_end ) * math.exp(-1. * frame_idx / cfg.epsilon_decay)
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self.policy_net = MLP(2*n_states, n_actions,cfg.hidden_dim).to(self.device)
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self.meta_policy_net = MLP(n_states, n_states,cfg.hidden_dim).to(self.device)
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self.optimizer = optim.Adam(self.policy_net.parameters(),lr=cfg.lr)
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self.meta_optimizer = optim.Adam(self.meta_policy_net.parameters(),lr=cfg.lr)
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self.memory = ReplayBuffer(cfg.memory_capacity)
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self.meta_memory = ReplayBuffer(cfg.memory_capacity)
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self.loss_numpy = 0
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self.meta_loss_numpy = 0
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self.losses = []
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self.meta_losses = []
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def to_onehot(self,x):
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oh = np.zeros(self.n_states)
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oh[x - 1] = 1.
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return oh
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def set_goal(self,state):
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if random.random() > self.epsilon(self.frame_idx):
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with torch.no_grad():
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state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(0)
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goal = self.meta_policy_net(state).max(1)[1].item()
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else:
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goal = random.randrange(self.n_states)
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return goal
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def choose_action(self,state):
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self.frame_idx += 1
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if random.random() > self.epsilon(self.frame_idx):
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with torch.no_grad():
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state = torch.tensor(state, device=self.device, dtype=torch.float32).unsqueeze(0)
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q_value = self.policy_net(state)
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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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self.update_policy()
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self.update_meta()
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def update_policy(self):
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if self.batch_size > len(self.memory):
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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)
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action_batch = torch.tensor(action_batch,device=self.device,dtype=torch.int64).unsqueeze(1)
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reward_batch = torch.tensor(reward_batch,device=self.device,dtype=torch.float)
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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)
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q_values = self.policy_net(state_batch).gather(dim=1, index=action_batch).squeeze(1)
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next_state_values = self.policy_net(next_state_batch).max(1)[0].detach()
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expected_q_values = reward_batch + 0.99 * next_state_values * (1-done_batch)
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loss = nn.MSELoss()(q_values, expected_q_values)
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self.optimizer.zero_grad()
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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()
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self.loss_numpy = loss.detach().cpu().numpy()
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self.losses.append(self.loss_numpy)
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def update_meta(self):
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if self.batch_size > len(self.meta_memory):
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return
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state_batch, action_batch, reward_batch, next_state_batch, done_batch = self.meta_memory.sample(self.batch_size)
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state_batch = torch.tensor(state_batch,device=self.device,dtype=torch.float)
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action_batch = torch.tensor(action_batch,device=self.device,dtype=torch.int64).unsqueeze(1)
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reward_batch = torch.tensor(reward_batch,device=self.device,dtype=torch.float)
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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)
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q_values = self.meta_policy_net(state_batch).gather(dim=1, index=action_batch).squeeze(1)
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next_state_values = self.meta_policy_net(next_state_batch).max(1)[0].detach()
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expected_q_values = reward_batch + 0.99 * next_state_values * (1-done_batch)
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meta_loss = nn.MSELoss()(q_values, expected_q_values)
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self.meta_optimizer.zero_grad()
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meta_loss.backward()
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for param in self.meta_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.meta_optimizer.step()
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self.meta_loss_numpy = meta_loss.detach().cpu().numpy()
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self.meta_losses.append(self.meta_loss_numpy)
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def save(self, path):
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torch.save(self.policy_net.state_dict(), path+'policy_checkpoint.pth')
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torch.save(self.meta_policy_net.state_dict(), path+'meta_checkpoint.pth')
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def load(self, path):
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self.policy_net.load_state_dict(torch.load(path+'policy_checkpoint.pth'))
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self.meta_policy_net.load_state_dict(torch.load(path+'meta_checkpoint.pth'))
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