帮我用Python和pytorch写个DQN算法用来训练PONG

好的，以下是一个简单的DQN算法用来训练PONG游戏的代码。请注意，这只是一个基本的实现，可以做更多的改进和优化。 首先，我们需要安装必要的库：pytorch和gym。 ```python !pip install torch !pip install gym ``` 接下来，我们将导入所需的库和定义一些超参数。 ```python import random import numpy as np import torch import torch.nn as nn import torch.optim as optim import gym # 超参数 BATCH_SIZE = 32 LEARNING_RATE = 0.0005 EPSILON = 1 EPSILON_DECAY = 0.9995 EPSILON_MIN = 0.01 GAMMA = 0.99 TARGET_UPDATE = 10 MEMORY_SIZE = 1000000 ``` 现在，我们将定义我们的DQN模型。 ```python class DQN(nn.Module): def __init__(self, input_dim, output_dim): super(DQN, self).__init__() self.fc1 = nn.Linear(input_dim, 128) self.fc2 = nn.Linear(128, 64) self.fc3 = nn.Linear(64, output_dim) def forward(self, x): x = nn.functional.relu(self.fc1(x)) x = nn.functional.relu(self.fc2(x)) x = self.fc3(x) return x ``` 然后，我们将定义我们的游戏环境和存储经验的经验回放缓冲区。 ```python env = gym.make('Pong-v0') input_dim = env.observation_space.shape[0] * env.observation_space.shape[1] * env.observation_space.shape[2] output_dim = env.action_space.n memory = [] ``` 接下来，我们将定义我们的DQN代理和目标网络，以及与之关联的优化器。 ```python agent = DQN(input_dim, output_dim) target = DQN(input_dim, output_dim) target.load_state_dict(agent.state_dict()) target.eval() optimizer = optim.Adam(agent.parameters(), lr=LEARNING_RATE) ``` 现在，我们将定义我们的动作选择函数，该函数将根据当前的epsilon值使用epsilon-greedy策略选择动作。 ```python def select_action(state, epsilon): if random.random() < epsilon: return env.action_space.sample() else: with torch.no_grad(): state = torch.from_numpy(state).float().unsqueeze(0) q_values = agent(state) return q_values.max(1)[1].item() ``` 然后，我们将定义我们的训练函数，该函数将从经验回放缓冲区中随机选择一批经验，并使用它们来更新我们的DQN代理。 ```python def train(): if len(memory) < BATCH_SIZE: return transitions = random.sample(memory, BATCH_SIZE) batch = Transition(*zip(*transitions)) state_batch = torch.from_numpy(np.stack(batch.state)).float() action_batch = torch.from_numpy(np.array(batch.action)).long() reward_batch = torch.from_numpy(np.array(batch.reward)).float() next_state_batch = torch.from_numpy(np.stack(batch.next_state)).float() done_batch = torch.from_numpy(np.array(batch.done)).float() q_values = agent(state_batch).gather(1, action_batch.unsqueeze(1)).squeeze(1) next_q_values = target(next_state_batch).max(1)[0].detach() expected_q_values = reward_batch + (1 - done_batch) * GAMMA * next_q_values loss = nn.functional.smooth_l1_loss(q_values, expected_q_values) optimizer.zero_grad() loss.backward() optimizer.step() ``` 最后，我们将定义我们的主要训练循环，该循环将在游戏中运行并更新我们的DQN代理和目标网络。 ```python Transition = np.dtype([('state', np.float32, (input_dim,)), ('action', np.int32), ('reward', np.float32), ('next_state', np.float32, (input_dim,)), ('done', np.bool)]) epsilon = EPSILON state = env.reset() while True: action = select_action(state, epsilon) next_state, reward, done, info = env.step(action) memory.append((state, action, reward, next_state, done)) if len(memory) > MEMORY_SIZE: del memory[0] state = next_state train() if done: state = env.reset() if env._episode_started_at is not None: episode_length = info['steps'] - env._episode_started_at if episode_length > 0: episode_reward = info['score'] print(f"Episode {len(memory)} - Reward: {episode_reward} - Steps: {episode_length}") if len(memory) % TARGET_UPDATE == 0: target.load_state_dict(agent.state_dict()) if epsilon > EPSILON_MIN: epsilon *= EPSILON_DECAY epsilon = max(EPSILON_MIN, epsilon) ``` 这就是我们的DQN算法的完整实现。您可以将其保存为.py文件并在控制台中运行。在训练过程中，您将看到每个回合的奖励和步骤数。请注意，训练可能需要一段时间，具体取决于您的计算机性能和超参数设置。