original_docstring: 
          Train the agent on the given proxy environment.
  
          :param proxy_env: the proxy gym environment
          :param nb_steps: number of training steps to be performed.
          :return: None
          
docstring: Train the agent on the given proxy environment.
docstring_tokens: Train the agent on the given proxy environment .
code: def fit(self, proxy_env: ProxyEnv, nb_steps: int) -> None:
          """
          Train the agent on the given proxy environment.
  
          :param proxy_env: the proxy gym environment
          :param nb_steps: number of training steps to be performed.
          :return: None
          """
          action_counter = 0
  
          proxy_env.reset()
          while action_counter < nb_steps:
              action = self._pick_an_action()
              obs, reward, done, info = proxy_env.step(action)
              self._update_model(obs, reward, done, info, action)
              action_counter += 1
              if action_counter % 10 == 0:
                  logger.info("Action: step_id='{}' action='{}' reward='{}'".format(action_counter, action, reward))
          proxy_env.close()
code_tokens: def fit ( self , proxy_env : ProxyEnv , nb_steps : int ) -> None : action_counter = 0 proxy_env . reset ( ) while action_counter < nb_steps : action = self . _pick_an_action ( ) obs , reward , done , info = proxy_env . step ( action ) self . _update_model ( obs , reward , done , info , action ) action_counter += 1 if action_counter % 10 == 0 : logger . info ( "Action: step_id='{}' action='{}' reward='{}'" . format ( action_counter , action , reward ) ) proxy_env . close ( )
short_docstring: Train the agent on the given proxy environment.
short_docstring_tokens: Train the agent on the given proxy environment .
comment: """
          Train the agent on the given proxy environment.
  
          :param proxy_env: the proxy gym environment
          :param nb_steps: number of training steps to be performed.
          :return: None
          """