class_docstr: Configurations of an environment.
  
  Args:
      action_space: An instance of :class:`~texar.tf.agents.Space` or
          :gym:`gym.Space <#spaces>`, the action space.
      observ_space: An instance of :class:`~texar.tf.agents.Space` or
          :gym:`gym.Space <#spaces>`, the observation space.
      reward_range: A tuple corresponding to the min and max possible
          rewards, e.g., `reward_range=(-1.0, 1.0)`.
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code: def __init__(self, action_space, observ_space, reward_range):
      if gym_utils:
          action_space = gym_utils.convert_gym_space(action_space)
          observ_space = gym_utils.convert_gym_space(observ_space)
  
      self.action_space = action_space
      self.action_dtype = action_space.dtype
      self.action_shape = action_space.shape
  
      self.observ_space = observ_space
      self.observ_dtype = observ_space.dtype
      self.observ_shape = observ_space.shape
  
      self.reward_range = reward_range
  
file_path: texar/texar/tf/agents/agent_utils.py
imports: import numpy
label_desc: Use image node_id 1 to create a new EnvConfig object from inherited base classes: object with example: obj = EnvConfig(action_space, observ_space, reward_range)