Pendulum ppo实现

Pendulum是一个经典的控制问题，可以使用PPO算法来解决。下面是Pendulum PPO实现的一个示例代码：

import gym
import numpy as np
import tensorflow as tf
import tensorflow_probability as tfp

tfd = tfp.distributions

# 定义神经网络模型
class Policy(tf.keras.Model):
    def __init__(self, num_actions):
        super(Policy, self).__init__()
        self.dense1 = tf.keras.layers.Dense(64, activation='relu')
        self.dense2 = tf.keras.layers.Dense(64, activation='relu')
        self.dense3 = tf.keras.layers.Dense(num_actions)

    def call(self, inputs):
        x = self.dense1(inputs)
        x = self.dense2(x)
        x = self.dense3(x)
        return tfd.Categorical(logits=x)

# 定义PPO算法的损失函数
def compute_loss(model, old_probs, states, actions, rewards, discount_factor, epsilon):
    # 计算新的策略分布
    new_probs = model(states)
    # 计算比率 r_t(theta) / r_{t-1}(theta)
    ratios = tf.exp(new_probs.log_prob(actions) - old_probs.log_prob(actions))
    # 计算重要性采样权重
    weights = tf.minimum(ratios, 1 + epsilon) * tf.maximum(ratios, 1 - epsilon)
    # 计算 PPO 的 surrogate loss
    advantages = rewards - tf.reduce_mean(rewards)
    surrogate_loss = -tf.reduce_mean(weights * advantages)
    # 计算 entropy bonus
    entropy_bonus = tf.reduce_mean(new_probs.entropy())
    # 计算总的损失函数
    total_loss = surrogate_loss - 0.01 * entropy_bonus
    return total_loss

# 定义训练函数
def train(env, model, optimizer, num_epochs, batch_size, discount_factor, epsilon):
    for epoch in range(num_epochs):
        # 初始化环境
        state = env.reset()
        done = False
        total_reward = 0
        # 初始化回合数据
        states = []
        actions = []
        rewards = []
        old_probs = []
        while not done:
            # 选择动作
            action_probs = model(tf.convert_to_tensor(state[None, :], dtype=tf.float32))
            action = action_probs.sample()[0].numpy()
            # 执行动作
            next_state, reward, done, _ = env.step(action)
            # 记录回合数据
            states.append(state)
            actions.append(action)
            rewards.append(reward)
            old_probs.append(action_probs.log_prob(action))
            # 更新状态和累计回报
            state = next_state
            total_reward += reward
        # 计算回合数据
        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)
        old_probs = tf.concat(old_probs, axis=0)
        # 计算损失函数
        with tf.GradientTape() as tape:
            loss = compute_loss(model, old_probs, states, actions, rewards, discount_factor, epsilon)
        # 反向传播更新参数
        gradients = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(gradients, model.trainable_variables))
        # 打印训练信息
        print('Epoch: {}, Total Reward: {:.2f}'.format(epoch + 1, total_reward))

# 创建环境
env = gym.make('Pendulum-v0')
num_actions = env.action_space.n
# 创建模型和优化器
model = Policy(num_actions)
optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
# 训练模型
train(env, model, optimizer, num_epochs=1000, batch_size=32, discount_factor=0.99, epsilon=0.2)

这段代码使用TensorFlow和TensorFlow Probability实现了Pendulum的PPO算法。其中，Policy类定义了一个神经网络模型，用于预测动作的概率分布；compute_loss函数定义了PPO算法的损失函数，包括surrogate loss和entropy bonus；train函数使用环境和模型训练PPO算法。在训练过程中，模型会不断地与环境交互，收集回合数据，并使用PPO算法更新模型参数。