class_docstr: Base class inherited by episodic RL agents.
  
  An agent is a wrapper of the **training process** that trains a model
  with RL algorithms. Agent itself does not create new trainable variables.
  
  An episodic RL agent typically provides 3 interfaces, namely, :meth:`reset`,
  :meth:`get_action` and :meth:`observe`, and is used as the following
  example.
  
  Example:
  
      .. code-block:: python
  
          env = SomeEnvironment(...)
          agent = PGAgent(...)
  
          while True:
              # Starts one episode
              agent.reset()
              observ = env.reset()
              while True:
                  action = agent.get_action(observ)
                  next_observ, reward, terminal = env.step(action)
                  agent.observe(reward, terminal)
                  observ = next_observ
                  if terminal:
                      break
  
  Args:
      env_config: An instance of :class:`~texar.tf.agents.EnvConfig`
          specifying action space, observation space, and reward range, etc.
          Use :func:`~texar.tf.agents.get_gym_env_config` to create an
          EnvConfig from a gym environment.
      hparams (dict or HParams, optional): Hyperparameters. Missing
          hyperparamerter will be set to default values. See
          :meth:`default_hparams` for the hyperparameter sturcture and
          default values.
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docstr: Environment configuration.
          
docstr_tok: ["Environment","configuration","."]
code: def env_config(self):
      """Environment configuration."""
      return self._env_config
  
file_path: texar/texar/tf/agents/episodic_agent_base.py
imports: import tensorflow
  from texar.tf.agents.agent_base import AgentBase
label_desc: Use image node_id 9 for calling the EpisodicAgentBase obj's underlying member method code with example usage: obj.env_config() and returns: self