pg_base.py 文件源码

def __init__(self, env_name, num_episodes, alpha, gamma, policy, **kwargs):
        """
        base class for RL using policy gradient
        :param env_name: name of environment, currently environments whose observation space is Box and action space is
         Discrete are supported. see https://github.com/openai/gym/wiki/Table-of-environments
        :param num_episodes:
        :param alpha:
        :param gamma:
        :param policy:
        :param kwargs:
        """
        super(PGBase, self).__init__(env_name, num_episodes, alpha, gamma, policy, **kwargs)
        if not isinstance(self.env.action_space, gym.spaces.Discrete) or \
                not isinstance(self.env.observation_space, gym.spaces.Box):
            raise NotImplementedError("action_space should be discrete and "
                                      "observation_space should be box")
        self.obs_shape = self.env.observation_space.shape
        self.obs_size = reduce(lambda x, y: x * y, self.obs_shape)
        self.action_size = self.env.action_space.n
        self._feature = None
        self._weight = None