TensorFlow RL 是 TensorFlow 的一部分,它提供了构建和训练强化学习模型所需的工具和库。以下是一个快速入门指南,帮助您开始使用 TensorFlow RL。

快速开始步骤

  1. 安装 TensorFlow RL 首先,确保您已经安装了 TensorFlow。然后,使用以下命令安装 TensorFlow RL:

    pip install tensorflow-reinforcement-learning
    
  2. 创建一个简单的环境 TensorFlow RL 提供了多种环境,例如 CartPoleMountainCar 等。以下是一个使用 CartPole 环境的示例:

    import gym
    import tensorflow as tf
    from tf_agents.environments import tf_py_environment
    from tf_agents.networks import q_network
    from tf_agents.agents.dqn import dqn_agent
    from tf_agents.replay_buffers import tf_uniform_replay_buffer
    from tf_agents.utils import common
    
    env_name = 'CartPole-v1'
    tf_env = tf_py_environment.PyEnvironment(gym.make(env_name))
    
  3. 定义网络和训练 创建一个 Q 网络,并使用 DQN 算法进行训练:

    num_iterations = 10000
    eval_interval = 50
    train_step_counter = tf.Variable(0)
    
    optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=1e-3)
    
    train_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
        data_spec=tf_env.time_step_spec().unstack(),
        batch_size=64,
        max_length=100000)
    
    actor_net = q_network.QNetwork(
        tf_env.observation_spec(),
        tf_env.action_spec(),
        fc_layer_params=(100,))
    
    agent = dqn_agent.DqnAgent(
        tf_env.time_step_spec(),
        tf_env.action_spec(),
        actor_net,
        optimizer=optimizer,
        td_errors_loss_fn=common.element_wise_squared_loss,
        train_step_counter=train_step_counter,
        replay_buffer=train_buffer)
    
    agent.initialize()
    
  4. 运行训练 开始训练过程,并定期评估模型:

    for _ in range(num_iterations):
        for _ in range(100):
            time_step = tf_env.reset()
            for _ in range(100):
                action = agent.action(time_step)
                next_time_step = tf_env.step(action)
                reward = next_time_step.reward
                train_buffer.add(time_step, action, reward, next_time_step)
        agent.train_step()
    
        if train_step_counter % eval_interval == 0:
            avg_return = 0
            for _ in range(10):
                time_step = tf_env.reset()
                while not time_step.is_last():
                    action = agent.action(time_step)
                    time_step = tf_env.step(action)
                avg_return += time_step.reward
            avg_return /= 10
            print('Step {}: Average Return = {}'.format(train_step_counter.numpy(), avg_return))
    
  5. 扩展阅读 想要了解更多关于 TensorFlow RL 的信息,请访问我们的 TensorFlow RL 教程

CartPole