def observe(self, terminal, reward, index=0):
        """
        Adds an observation (reward and is-terminal) to the model without updating its trainable variables.

        Args:
            terminal (List[bool]): List of is-terminal signals.
            reward (List[float]): List of reward signals.
            index: (int) parallel episode you want to observe

        Returns:
            The value of the model-internal episode counter.
        """
        fetches = self.episode_output
        feed_dict = self.get_feed_dict(terminal=terminal, reward=reward, index=index)

        episode = self.monitored_session.run(fetches=fetches, feed_dict=feed_dict)

        return episode
def observe(self, terminal, reward, index=0):
        """
        Adds an observation (reward and is-terminal) to the model without updating its trainable variables.

        Args:
            terminal (List[bool]): List of is-terminal signals.
            reward (List[float]): List of reward signals.
            index: (int) parallel episode you want to observe

        Returns:
            The value of the model-internal episode counter.
        """
        fetches = self.episode_output
        feed_dict = self.get_feed_dict(terminal=terminal, reward=reward, index=index)

        episode = self.monitored_session.run(fetches=fetches, feed_dict=feed_dict)

        return episode
def observe ( self , terminal , reward , index = 0 ) : fetches = self . episode_output feed_dict = self . get_feed_dict ( terminal = terminal , reward = reward , index = index ) episode = self . monitored_session . run ( fetches = fetches , feed_dict = feed_dict ) return episode
Adds an observation (reward and is-terminal) to the model without updating its trainable variables.

        Args:
            terminal (List[bool]): List of is-terminal signals.
            reward (List[float]): List of reward signals.
            index: (int) parallel episode you want to observe

        Returns:
            The value of the model-internal episode counter.
Adds an observation ( reward and is - terminal ) to the model without updating its trainable variables .