写一段DQN训练Hero-ram-v0的代码

下面是一个使用深度强化学习（DQN）算法训练 OpenAI Gym 中的 `Hero-ram-v0` 环境的示例代码：

import gym
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.optimizers import Adam

# 创建 DQN 模型
def create_model(state_shape, action_size):
    model = Sequential()
    model.add(Dense(24, input_shape=state_shape, activation='relu'))
    model.add(Dense(24, activation='relu'))
    model.add(Dense(action_size, activation='linear'))
    model.compile(loss='mse', optimizer=Adam(learning_rate=0.001))
    return model

# 初始化经验回放缓冲区
class ReplayBuffer:
    def __init__(self, buffer_size):
        self.buffer_size = buffer_size
        self.buffer = []

    def add(self, experience):
        self.buffer.append(experience)
        if len(self.buffer) > self.buffer_size:
            self.buffer.pop(0)

    def sample(self, batch_size):
        return np.random.choice(self.buffer, batch_size)

# DQN Agent
class DQNAgent:
    def __init__(self, state_shape, action_size, buffer_size):
        self.state_shape = state_shape
        self.action_size = action_size
        self.buffer = ReplayBuffer(buffer_size)
        self.model = create_model(state_shape, action_size)

    def act(self, state):
        state = np.expand_dims(state, axis=0)
        q_values = self.model.predict(state)[0]
        action = np.argmax(q_values)
        return action

    def train(self, batch_size, gamma):
        minibatch = self.buffer.sample(batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                next_state = np.expand_dims(next_state, axis=0)
                target = reward + gamma * np.amax(self.model.predict(next_state)[0])

            state = np.expand_dims(state, axis=0)
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, epochs=1, verbose=0)

    def remember(self, state, action, reward, next_state, done):
        experience = (state, action, reward, next_state, done)
        self.buffer.add(experience)

# 创建环境和 agent
env = gym.make('Hero-ram-v0')
state_shape = env.observation_space.shape
action_size = env.action_space.n
agent = DQNAgent(state_shape, action_size, buffer_size=1000)

# 训练 DQN agent
num_episodes = 1000
batch_size = 32
gamma = 0.99

for episode in range(num_episodes):
    state = env.reset()
    done = False
    total_reward = 0

    while not done:
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        agent.remember(state, action, reward, next_state, done)
        state = next_state
        total_reward += reward

    if len(agent.buffer.buffer) > batch_size:
        agent.train(batch_size, gamma)

    print(f"Episode: {episode+1}, Reward: {total_reward}")

# 使用训练好的 agent 进行测试
num_test_episodes = 10

for episode in range(num_test_episodes):
    state = env.reset()
    done = False
    total_reward = 0

    while not done:
        action = agent.act(state)
        state, reward, done, _ = env.step(action)
        total_reward += reward

    print(f"Test Episode: {episode+1}, Reward: {total_reward}")

请确保已经安装了 Gym、NumPy 和 TensorFlow 库。该代码使用一个简单的神经网络作为 DQN 的近似函数，并使用经验回放缓冲区来存储和重放过去的经验。在训练过程中，agent 与环境交互，并使用 Q-learning 更新网络权重。最后，代码还提供了一个简单的测试环节，用于评估训练好的 agent 在环境中的性能。