强化学习 - Proximal Policy Optimization (PPO)

什么是机器学习

Proximal Policy Optimization（PPO）是一种用于解决强化学习问题的策略梯度方法。PPO的目标是通过优化代理的策略函数来最大化期望累积奖励。它通过引入一个剪切项（clipping term）来限制新策略相对于旧策略的更新幅度，从而提高训练的稳定性。

以下是一个使用 Python 和 TensorFlow/Keras 实现简单的 Proximal Policy Optimization（PPO）的示例。在这个例子中，我们将使用 OpenAI Gym 的 CartPole 环境。

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.optimizers import Adam
import gym# 定义Proximal Policy Optimization Agent
class PPOAgent:def __init__(self, state_size, action_size):self.state_size = state_sizeself.action_size = action_sizeself.gamma = 0.99  # 折扣因子self.learning_rate = 0.001self.clip_ratio = 0.2self.epochs = 10# 构建演员（Actor）网络self.actor = self.build_actor()# 构建评论家（Critic）网络self.critic = self.build_critic()def build_actor(self):state_input = Input(shape=(self.state_size,))dense1 = Dense(64, activation='relu')(state_input)dense2 = Dense(64, activation='relu')(dense1)output = Dense(self.action_size, activation='softmax')(dense2)model = Model(inputs=state_input, outputs=output)model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=self.learning_rate))return modeldef build_critic(self):state_input = Input(shape=(self.state_size,))dense1 = Dense(64, activation='relu')(state_input)dense2 = Dense(64, activation='relu')(dense1)output = Dense(1, activation='linear')(dense2)model = Model(inputs=state_input, outputs=output)model.compile(loss='mean_squared_error', optimizer=Adam(lr=self.learning_rate))return modeldef get_action(self, state):state = np.reshape(state, [1, self.state_size])action_prob = self.actor.predict(state)[0]action = np.random.choice(self.action_size, p=action_prob)return action, action_probdef get_value(self, state):state = np.reshape(state, [1, self.state_size])value = self.critic.predict(state)[0][0]return valuedef train(self, states, actions, rewards, values, dones):advantages, target_values = self.compute_advantages_and_targets(rewards, values, dones)# 训练演员网络old_action_probs = self.actor.predict(np.vstack(states))old_action_probs = np.array([probs[action] for probs, action in zip(old_action_probs, actions)])action_one_hot = tf.keras.utils.to_categorical(actions, self.action_size)action_probs = self.actor.predict(np.vstack(states))action_probs = np.array([probs[action] for probs, action in zip(action_probs, actions)])ratio = np.exp(np.log(action_probs) - np.log(old_action_probs))clipped_ratio = np.clip(ratio, 1 - self.clip_ratio, 1 + self.clip_ratio)actor_loss = -np.mean(np.minimum(ratio * advantages, clipped_ratio * advantages))self.actor.train_on_batch(np.vstack(states), action_one_hot, sample_weight=ratio / clipped_ratio)# 训练评论家网络self.critic.train_on_batch(np.vstack(states), target_values)def compute_advantages_and_targets(self, rewards, values, dones):advantages = np.zeros_like(rewards, dtype=np.float32)target_values = np.zeros_like(rewards, dtype=np.float32)running_add = 0for t in reversed(range(len(rewards))):running_add = running_add * self.gamma * (1 - dones[t]) + rewards[t]advantages[t] = running_add - values[t]target_values[t] = running_addreturn advantages, target_values# 初始化环境和Agent
env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
agent = PPOAgent(state_size, action_size)# 训练PPO Agent
num_episodes = 1000
for episode in range(num_episodes):state = env.reset()total_reward = 0states, actions, rewards, values, dones = [], [], [], [], []for time in range(500):  # 限制每个episode的步数，防止无限循环# env.render()  # 如果想可视化训练过程，可以取消注释此行action, action_prob = agent.get_action(state)next_state, reward, done, _ = env.step(action)total_reward += rewardvalue = agent.get_value(state)states.append(state)actions.append(action)rewards.append(reward)values.append(value)dones.append(done)state = next_stateif done:print("Episode: {}, Total Reward: {}".format(episode + 1, total_reward))agent.train(states, actions, rewards, values, dones)break# 关闭环境
env.close()