observation(object): an environment-specific object representing your observation of the environment. For example, pixel data from a camera, joint angles and joint velocities of a robot, or the board state in a board game. reward(float): amount of reward achieved by the previous action. The s...
classMasterAgent():def__init__(self): self.game_name = 'CartPole-v0' save_dir = args.save_dir self.save_dir = save_dirifnot os.path.exists(save_dir): os.makedirs(save_dir) env = gym.make(self.game_name) self.state_size = env.observation_space.shape[0] se...
AI代码解释 classMasterAgent():def__init__(self):self.game_name='CartPole-v0'save_dir=args.save_dir self.save_dir=save_dirifnot os.path.exists(save_dir):os.makedirs(save_dir)env=gym.make(self.game_name)self.state_size=env.observation_space.shape[0]self.action_size=env.action_space.n...
研究人员使用 tf.keras、OpenAI 训练了一个使用「异步优势动作评价」(Asynchronous Advantage Actor Critic,A3C)算法的智能体,通过 A3C 的实现解决了 CartPole 游戏问题,过程中使用了贪婪执行、模型子类和自定义训练循环。 该过程围绕以下概念运行: 贪婪执行——贪婪执行是一个必要的、由运行定义的接口,此处的运算一旦...
def run_game(env,model,generation,render=False,save=False): """ Play one cartpole game given a trained model Attributes: --- render: if True, render the gameplay save: if True save the gameplay in mp4 format """ obs = env.reset() obs = np.reshape(obs, [1, 4]) env._max_episod...
we can predict how slippery a surface would be to walk on just by looking at it etc. By using our "transition model", i.e. our understanding of "the rules of the game", we can plan accordingly and take actions to achieve our goals. We learn this "transition model" through our own...
问DQN算法在CartPole-v0上不收敛EN在强化学习（八）价值函数的近似表示与Deep Q-Learning中，我们讲到了...
本教程讲解如何使用深度强化学习训练一个可以在 CartPole 游戏中获胜的模型。研究人员使用 tf.keras、OpenAI 训练了一个使用「异步优势动作评价」(Asynchronous Advantage Actor Critic,A3C)算法的智能体,通过 A3C 的实现解决了 CartPole 游戏问题,过程中使用了贪婪执行、模型子类和自定义训练循环。
本教程讲解如何使用深度强化学习训练一个可以在 CartPole 游戏中获胜的模型。研究人员使用 tf.keras、OpenAI 训练了一个使用「异步优势动作评价」(Asynchronous Advantage Actor Critic,A3C)算法的智能体,通过 A3C 的实现解决了 CartPole 游戏问题,过程中使用了贪婪执行、模型子类和自定义训练循环。
OpenAI Gym Cartpole game using DQNInstallationsUtilitiesDefining the model and agent classes License This Notebook has been released under the Apache 2.0 open source license. Continue exploring Input1 file arrow_right_alt Output1 file arrow_right_alt Logs4382.4 second run - successful arrow_right_alt...