dqn常见的双移线代码,举例说明
时间: 2023-04-11 07:01:58 浏览: 53
以下是一个常见的DQN双移线代码示例:
```python
# 定义目标网络和估计网络
target_net = DQN()
eval_net = DQN()
# 定义优化器和损失函数
optimizer = torch.optim.Adam(eval_net.parameters(), lr=LR)
loss_func = nn.MSELoss()
# 定义双移线所需的参数
memory_counter = 0
memory = np.zeros((MEMORY_CAPACITY, N_STATES * 2 + 2))
target_update_counter = 0
# 开始训练
for i_episode in range(MAX_EPISODES):
state = env.reset()
ep_r = 0
while True:
# 选择动作
action = choose_action(state)
# 执行动作
next_state, reward, done, info = env.step(action)
# 记录记忆
memory[memory_counter, :] = np.hstack((state, [action, reward], next_state))
memory_counter += 1
if memory_counter > MEMORY_CAPACITY:
memory_counter = 0
# 更新网络
if memory_counter > BATCH_SIZE:
sample_index = np.random.choice(MEMORY_CAPACITY, BATCH_SIZE)
b_memory = memory[sample_index, :]
b_state = torch.FloatTensor(b_memory[:, :N_STATES])
b_action = torch.LongTensor(b_memory[:, N_STATES:N_STATES+1].astype(int))
b_reward = torch.FloatTensor(b_memory[:, N_STATES+1:N_STATES+2])
b_next_state = torch.FloatTensor(b_memory[:, -N_STATES:])
q_eval = eval_net(b_state).gather(1, b_action)
q_next = target_net(b_next_state).detach()
q_target = b_reward + GAMMA * q_next.max(1)[0].view(BATCH_SIZE, 1)
loss = loss_func(q_eval, q_target)
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 更新目标网络
target_update_counter += 1
if target_update_counter % TARGET_REPLACE_ITER == 0:
target_net.load_state_dict(eval_net.state_dict())
# 更新状态和累计奖励
state = next_state
ep_r += reward
if done:
break
# 打印训练结果
print('Episode:', i_episode, ' Reward: %i' % int(ep_r))
```