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
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
创建一个简单的环境 TensorFlow RL 提供了多种环境,例如
CartPole
、MountainCar
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))
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()
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))
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