ppo算法python实现

PPO（Proximal Policy Optimization）算法是一种强化学习算法，用于训练策略优化模型。下面是一个使用Python实现PPO算法的简单示例代码：

import gym
import numpy as np
import tensorflow as tf

class PPOAgent:
    def __init__(self, state_dim, action_dim, action_bound):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.action_bound = action_bound
        
        self.actor = self.build_actor()
        self.critic = self.build_critic()
        self.actor_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
        self.critic_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
        
    def build_actor(self):
        model = tf.keras.Sequential([
            tf.keras.layers.Dense(64, activation='relu', input_shape=(self.state_dim,)),
            tf.keras.layers.Dense(64, activation='relu'),
            tf.keras.layers.Dense(self.action_dim, activation='tanh')
        ])
        return model
    
    def build_critic(self):
        model = tf.keras.Sequential([
            tf.keras.layers.Dense(64, activation='relu', input_shape=(self.state_dim,)),
            tf.keras.layers.Dense(64, activation='relu'),
            tf.keras.layers.Dense(1)
        ])
        return model
    
    def choose_action(self, state):
        state = np.expand_dims(state, axis=0)
        action = self.actor.predict(state)[0]
        action = np.clip(action, -self.action_bound, self.action_bound)
        return action
    
    def compute_loss(self, states, actions, advantages, old_probs):
        with tf.GradientTape() as tape:
            new_probs = self.actor(states, training=True)
            critic_values = tf.squeeze(self.critic(states, training=True), axis=-1)
            
            ratio = tf.exp(new_probs - old_probs)
            clipped_ratio = tf.clip_by_value(ratio, 1 - epsilon, 1 + epsilon)
            surrogate_1 = ratio * advantages
            surrogate_2 = clipped_ratio * advantages
            actor_loss = -tf.reduce_mean(tf.minimum(surrogate_1, surrogate_2))
            
            critic_loss = tf.reduce_mean(tf.square(critic_values - advantages))
            total_loss = actor_loss + critic_loss
            
        actor_gradients = tape.gradient(total_loss, self.actor.trainable_variables)
        critic_gradients = tape.gradient(critic_loss, self.critic.trainable_variables)
        self.actor_optimizer.apply_gradients(zip(actor_gradients, self.actor.trainable_variables))
        self.critic_optimizer.apply_gradients(zip(critic_gradients, self.critic.trainable_variables))
    
    def train(self, env, batch_size=64, epochs=10, gamma=0.99, epsilon=0.2):
        for epoch in range(epochs):
            states = []
            actions = []
            rewards = []
            values = []
            old_probs = []
            
            state = env.reset()
            while True:
                action = self.choose_action(state)
                next_state, reward, done, _ = env.step(action)
                
                states.append(state)
                actions.append(action)
                rewards.append(reward)
                values.append(self.critic.predict(np.expand_dims(state, axis=0))[0])
                old_probs.append(self.actor.predict(np.expand_dims(state, axis=0))[0])
                
                state = next_state
                
                if done:
                    break
            
            states = np.array(states)
            actions = np.array(actions)
            rewards = np.array(rewards)
            values = np.array(values)
            old_probs = np.array(old_probs)
            
            returns = np.zeros_like(rewards)
            advantages = np.zeros_like(rewards)
            
            if not done:
                last_value = self.critic.predict(np.expand_dims(state, axis=0))[0]
            else:
                last_value = 0
                
            for t in reversed(range(len(rewards))):
                returns[t] = rewards[t] + gamma * last_value
                last_value = returns[t]
                
                advantages[t] = returns[t] - values[t]
            
            advantages = (advantages - advantages.mean()) / (advantages.std() + 1e-8)
            
            for _ in range(len(states) // batch_size):
                indices = np.random.choice(len(states), batch_size)
                batch_states = states[indices]
                batch_actions = actions[indices]
                batch_advantages = advantages[indices]
                batch_old_probs = old_probs[indices]
                
                self.compute_loss(batch_states, batch_actions, batch_advantages, batch_old_probs)

这是一个简单的PPO算法实现示例，使用了tensorflow框架。需要注意的是，代码中的`state_dim`表示状态维度，`action_dim`表示动作维度，`action_bound`表示动作的取值范围。你可以根据你的具体问题进行调整和修改。此外，你还需要安装相应的依赖库（如gym和tensorflow）。希望对你有所帮助！