a3c代码

以下是一个简单的 A3C 实现代码，仅供参考：

import gym
import numpy as np
import tensorflow as tf
import threading

global_episode = 0
global_rewards = []
global_episodes = 10000
episode_rewards = tf.keras.metrics.Mean('episode_rewards', dtype=tf.float32)

class A3C(tf.keras.Model):
    def __init__(self, state_size, action_size):
        super(A3C, self).__init__()
        self.state_size = state_size
        self.action_size = action_size
        self.dense1 = tf.keras.layers.Dense(64, activation='relu')
        self.dense2 = tf.keras.layers.Dense(64, activation='relu')
        self.policy_logits = tf.keras.layers.Dense(action_size)
        self.values = tf.keras.layers.Dense(1)

    def call(self, inputs):
        x = self.dense1(inputs)
        x = self.dense2(x)
        logits = self.policy_logits(x)
        values = self.values(x)
        return logits, values

class Agent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.global_model = A3C(state_size, action_size)
        self.global_model(tf.keras.Input(shape=(state_size,)))
        self.opt = tf.optimizers.Adam(learning_rate=.0001, clipnorm=1.0)
        self.gamma = 0.99
        self.tau = .125

    def train(self, state, action, reward, next_state, done):
        with tf.GradientTape() as tape:
            logits, value = self.global_model(tf.convert_to_tensor(state[None, :], dtype=tf.float32))
            next_logits, next_value = self.global_model(tf.convert_to_tensor(next_state[None, :], dtype=tf.float32))
            advantage = reward + self.gamma * next_value[0] * (1 - int(done)) - value[0]
            value_loss = advantage ** 2
            policy = tf.nn.softmax(logits)
            entropy = tf.reduce_sum(policy * tf.math.log(policy))
            policy_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=action, logits=logits)
            total_loss = tf.reduce_mean(.5 * value_loss + policy_loss - .01 * entropy)
        grads = tape.gradient(total_loss, self.global_model.trainable_variables)
        self.opt.apply_gradients(zip(grads, self.global_model.trainable_variables))

    def get_action(self, state):
        logits, _ = self.global_model(tf.convert_to_tensor(state[None, :], dtype=tf.float32))
        probs = tf.nn.softmax(logits)
        action = np.random.choice(self.action_size, p=probs.numpy()[0])
        return action

    def sync(self, local_model):
        for local, global_ in zip(local_model.trainable_variables, self.global_model.trainable_variables):
            global_.assign(self.tau * local + (1 - self.tau) * global_)

def test(env, agent):
    state = env.reset()
    done = False
    total_reward = 0
    while not done:
        action = agent.get_action(state)
        next_state, reward, done, _ = env.step(action)
        state = next_state
        total_reward += reward
    return total_reward

def train(global_agent, num_episodes, lock):
    global global_episode, global_rewards
    env = gym.make('CartPole-v0')
    agent = Agent(env.observation_space.shape[0], env.action_space.n)
    for ep in range(num_episodes):
        state = env.reset()
        done = False
        episode_reward = 0
        while not done:
            action = agent.get_action(state)
            next_state, reward, done, _ = env.step(action)
            agent.train(state, action, reward, next_state, done)
            state = next_state
            episode_reward += reward
        with lock:
            global_rewards.append(episode_reward)
            global_episode += 1
            episode_rewards(episode_reward)
            print("Episode: {}, Reward: {}".format(global_episode, episode_reward))
        agent.sync(agent)
        if global_episode % 100 == 0:
            test_reward = test(env, agent)
            print("Test Reward: {}".format(test_reward))

if __name__ == '__main__':
    lock = threading.Lock()
    global_agent = Agent(4, 2)
    threads = []
    for i in range(4):
        t = threading.Thread(target=train, args=(global_agent, global_episodes//4, lock))
        threads.append(t)
    for thread in threads:
        thread.start()
    for thread in threads:
        thread.join()

在这个实现中，我们首先定义了一个 A3C 模型和一个 Agent 类，其中 A3C 模型有两个输出：一个是策略 logits，一个是状态值估计。Agent 类负责在环境中与模型进行交互，以及使用梯度下降更新模型。

我们使用了一个简单的 CartPole 环境来测试模型。在训练过程中，我们创建了四个线程来并行地训练模型，每个线程都有自己的 local 模型。每个 episode 结束时，local 模型的参数会同步到 global 模型中。

此外，我们还定义了一个 test 函数来测试模型的性能。在每个训练周期的末尾，我们都会调用这个函数来评估模型在测试集上的表现。

请注意，这只是一个简单的实现，无法保证在所有环境中都能正常运行。如果你想要在自己的项目中使用 A3C，建议参考一些开源的实现，如 Tensorflow 的官方实现。