134 lines
5.8 KiB
Python
134 lines
5.8 KiB
Python
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.autograd as autograd
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import random
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import math
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class CNN(nn.Module):
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def __init__(self, input_dim, output_dim):
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super(CNN, self).__init__()
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self.input_dim = input_dim
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self.output_dim = output_dim
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self.features = nn.Sequential(
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nn.Conv2d(input_dim[0], 32, kernel_size=8, stride=4),
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nn.ReLU(),
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nn.Conv2d(32, 64, kernel_size=4, stride=2),
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nn.ReLU(),
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nn.Conv2d(64, 64, kernel_size=3, stride=1),
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nn.ReLU()
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)
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self.fc = nn.Sequential(
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nn.Linear(self.feature_size(), 512),
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nn.ReLU(),
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nn.Linear(512, self.output_dim)
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)
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def forward(self, x):
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x = self.features(x)
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x = x.view(x.size(0), -1)
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x = self.fc(x)
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return x
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def feature_size(self):
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return self.features(autograd.Variable(torch.zeros(1, *self.input_dim))).view(1, -1).size(1)
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def act(self, state, epsilon):
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if random.random() > epsilon:
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state = Variable(torch.FloatTensor(np.float32(state)).unsqueeze(0), volatile=True)
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q_value = self.forward(state)
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action = q_value.max(1)[1].data[0]
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else:
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action = random.randrange(env.action_space.n)
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return action
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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 DQN:
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def __init__(self, n_states, n_actions, cfg):
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self.n_actions = n_actions # 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.frame_idx = 0 # 用于epsilon的衰减计数
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self.epsilon = lambda frame_idx: cfg.epsilon_end + \
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(cfg.epsilon_start - cfg.epsilon_end) * \
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math.exp(-1. * frame_idx / cfg.epsilon_decay)
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self.batch_size = cfg.batch_size
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self.policy_net = CNN(n_states, n_actions).to(self.device)
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self.target_net = CNN(n_states, n_actions).to(self.device)
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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
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target_param.data.copy_(param.data)
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self.optimizer = optim.Adam(self.policy_net.parameters(), lr=cfg.lr) # 优化器
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self.memory = ReplayBuffer(cfg.memory_capacity) # 经验回放
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def choose_action(self, state):
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''' 选择动作
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'''
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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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print(type(state))
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state = torch.tensor([state], device=self.device, dtype=torch.float32)
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q_values = self.policy_net(state)
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action = q_values.max(1)[1].item() # 选择Q值最大的动作
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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: # 当memory中不满足一个批量时，不更新策略
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return
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# 从经验回放中(replay memory)中随机采样一个批量的转移(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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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).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) # 计算当前状态(s_t,a)对应的Q(s_t, a)
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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
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# 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
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expected_q_values = reward_batch + self.gamma * next_q_values * (1-done_batch)
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loss = nn.MSELoss()(q_values, expected_q_values.unsqueeze(1)) # 计算均方根损失
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# 优化更新模型
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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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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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for target_param, param in zip(self.target_net.parameters(), self.policy_net.parameters()):
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param.data.copy_(target_param.data) |