import gym
import numpy as np
import tensorflow as tf
from tensorflow.keras.layers import Dense, Input
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
import threading
import multiprocessing
import os# 定义Actor-Critic模型
class ActorCriticModel(tf.keras.Model):def __init__(self, state_size, action_size):super(ActorCriticModel, self).__init__()self.state_size = state_sizeself.action_size = action_sizeself.dense1 = Dense(128, activation='relu') # 第一个隐藏层self.policy_logits = Dense(action_size) # 输出动作概率的层self.dense2 = Dense(128, activation='relu') # 第二个隐藏层self.values = Dense(1) # 输出状态值的层def call(self, inputs):x = self.dense1(inputs)logits = self.policy_logits(x) # 计算动作概率v = self.dense2(inputs)values = self.values(v) # 计算状态值return logits, values# 训练函数
def train(global_model, optimizer, global_step):env = gym.make('CartPole-v1') # 创建CartPole环境，替换为实际环境名称max_episodes = 10000 # 最大训练次数gamma = 0.99 # 折扣因子update_freq = 5 # 更新频率num_workers = multiprocessing.cpu_count() # 并发智能体数for episode in range(max_episodes):state = env.reset()state = np.reshape(state, [1, state_size])with tf.GradientTape() as tape:total_reward = 0num_steps = 0done = Falsewhile not done:num_steps += 1logits, values = global_model(state)probs = tf.nn.softmax(logits) # 使用softmax计算动作概率action = np.random.choice(action_size, p=probs.numpy()[0]) # 根据概率随机选择动作next_state, reward, done, _ = env.step(action)next_state = np.reshape(next_state, [1, state_size])total_reward += rewardif done or num_steps >= 300: # 限制最大步数total_reward = -100 if not done else total_rewardR = 0else:_, R = global_model(next_state) # 获取下一状态的值td_target = reward + gamma * Rtd_error = td_target - valuesactor_loss = -tf.math.log(probs[0, action]) * td_error # Actor的损失critic_loss = tf.square(td_error) # Critic的损失total_loss = actor_loss + critic_loss # 综合Actor和Critic的损失grads = tape.gradient(total_loss, global_model.trainable_variables) # 计算梯度optimizer.apply_gradients(zip(grads, global_model.trainable_variables)) # 更新参数if global_step % update_freq == 0:global_model.set_weights(model.get_weights()) # 更新全局模型参数global_step += 1state = next_stateprint(f"Episode {episode+1}: Total Reward = {total_reward}")if __name__ == "__main__":state_size = 4 # 状态空间维度，替换为实际状态空间维度action_size = 2 # 动作空间维度，替换为实际动作空间维度global_model = ActorCriticModel(state_size, action_size) # 创建全局模型optimizer = Adam(learning_rate=0.001) # Adam优化器，替换为实际学习率global_step = 0workers = []for _ in range(num_workers):worker = threading.Thread(target=train, args=(global_model, optimizer, global_step))workers.append(worker)for worker in workers:worker.start()for worker in workers:worker.join()
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