强化学习代码连续动作

对于强化学习中的连续动作问题，常用的方法是使用深度强化学习算法和确定性策略梯度算法。以下是一个示例代码，演示如何使用Proximal Policy Optimization (PPO)算法来处理连续动作空间。

import gym
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers

# 定义Actor模型，输出均值和标准差
class Actor(tf.keras.Model):
    def __init__(self, num_actions):
        super(Actor, self).__init__()
        self.hidden1 = layers.Dense(64, activation='relu')
        self.hidden2 = layers.Dense(64, activation='relu')
        self.mu = layers.Dense(num_actions, activation='tanh')
        self.sigma = layers.Dense(num_actions, activation='softplus')

    def call(self, inputs):
        x = self.hidden1(inputs)
        x = self.hidden2(x)
        mu = self.mu(x)
        sigma = self.sigma(x)
        return mu, sigma

# 定义Critic模型，评估状态值函数
class Critic(tf.keras.Model):
    def __init__(self):
        super(Critic, self).__init__()
        self.hidden1 = layers.Dense(64, activation='relu')
        self.hidden2 = layers.Dense(64, activation='relu')
        self.value = layers.Dense(1)

    def call(self, inputs):
        x = self.hidden1(inputs)
        x = self.hidden2(x)
        value = self.value(x)
        return value

# 定义PPO算法的Agent
class PPOAgent:
    def __init__(self, num_actions):
        self.num_actions = num_actions
        self.actor = Actor(num_actions)
        self.critic = Critic()
        self.optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)

    def get_action(self, state):
        state = tf.convert_to_tensor([state], dtype=tf.float32)
        mu, sigma = self.actor(state)
        action_prob = tfp.distributions.Normal(mu, sigma)
        action = action_prob.sample(1)[0]
        return np.clip(action, -1, 1)

    def update(self, states, actions, rewards, log_probs, advantage):
        states = tf.convert_to_tensor(states, dtype=tf.float32)
        actions = tf.convert_to_tensor(actions, dtype=tf.float32)
        rewards = tf.convert_to_tensor(rewards, dtype=tf.float32)
        log_probs = tf.convert_to_tensor(log_probs, dtype=tf.float32)
        advantage = tf.convert_to_tensor(advantage, dtype=tf.float32)

        with tf.GradientTape() as tape:
            mu, sigma = self.actor(states)
            new_action_prob = tfp.distributions.Normal(mu, sigma)
            new_log_probs = new_action_prob.log_prob(actions)

            ratio = tf.exp(new_log_probs - log_probs)
            surrogate1 = ratio * advantage
            surrogate2 = tf.clip_by_value(ratio, 1.0 - 0.2, 1.0 + 0.2) * advantage
            actor_loss = -tf.reduce_mean(tf.minimum(surrogate1, surrogate2))

            target_values = rewards + 0.99 * self.critic(states)
            critic_loss = tf.reduce_mean(tf.square(target_values - self.critic(states)))

            total_loss = actor_loss + 0.5 * critic_loss

        actor_gradients = tape.gradient(total_loss, self.actor.trainable_variables)
        self.optimizer.apply_gradients(zip(actor_gradients, self.actor.trainable_variables))

        critic_gradients = tape.gradient(critic_loss, self.critic.trainable_variables)
        self.optimizer.apply_gradients(zip(critic_gradients, self.critic.trainable_variables))

# 创建环境和Agent
env = gym.make('Pendulum-v0')
agent = PPOAgent(num_actions=env.action_space.shape[0])

# 进行PPO算法的训练
for episode in range(1000):
    state = env.reset()
    episode_reward = 0
    states, actions, rewards, log_probs, = [], [], [], []
    done = False

    while not done:
        action = agent.get_action(state)
        next_state, reward, done, _ = env.step(action)
        log_prob = tfp.distributions.Normal(agent.actor(state)[0], agent.actor(state)[1]).log_prob(action)

        states.append(state)
        actions.append(action)
        rewards.append(reward)
        log_probs.append(log_prob)

        state = next_state
        episode_reward += reward

    advantage = np.zeros_like(rewards)
    target_value = 0
    for i in reversed(range(len(rewards))):
        delta = rewards[i] + 0.99 * target_value - agent.critic(np.array([states[i]], dtype=np.float32))
        target_value = agent.critic(np.array([states[i]], dtype=np.float32))
        advantage[i] = delta + 0.99 * 0.95 * advantage[i + 1]

    advantage = (advantage - np.mean(advantage)) / np.std(advantage)

    agent.update(states, actions, rewards, log_probs, advantage)

    print('Episode: {}, Reward: {:.2f}'.format(episode+1, episode_reward))

这段代码使用了TensorFlow和gym库，首先定义了一个Actor模型和一个Critic模型，分别用于输出动作的均值和标准差以及评估状态值函数。然后，定义了PPOAgent类作为PPO算法的实现。在训练过程中，通过调用get_action方法获取动作，并使用update方法更新模型参数。最后，通过循环执行训练过程，并输出每个episode的奖励。

请注意，这只是一个示例代码，具体问题的实施需要根据任务和环境进行适当的调整和修改。