def reset(self):
        """
        Resets the model to its initial state on episode start. This should also reset all preprocessor(s).

        Returns:
            tuple:
                Current episode, timestep counter and the shallow-copied list of internal state initialization Tensors.
        """
        fetches = [self.global_episode, self.global_timestep]

        # Loop through all preprocessors and reset them as well.
        for name in sorted(self.states_preprocessing):
            fetch = self.states_preprocessing[name].reset()
            if fetch is not None:
                fetches.extend(fetch)

        if self.flush_summarizer is not None:
            fetches.append(self.flush_summarizer)

        # Get the updated episode and timestep counts.
        fetch_list = self.monitored_session.run(fetches=fetches)
        episode, timestep = fetch_list[:2]

        return episode, timestep, self.internals_init
def reset(self):
        """
        Resets the model to its initial state on episode start. This should also reset all preprocessor(s).

        Returns:
            tuple:
                Current episode, timestep counter and the shallow-copied list of internal state initialization Tensors.
        """
        fetches = [self.global_episode, self.global_timestep]

        # Loop through all preprocessors and reset them as well.
        for name in sorted(self.states_preprocessing):
            fetch = self.states_preprocessing[name].reset()
            if fetch is not None:
                fetches.extend(fetch)

        if self.flush_summarizer is not None:
            fetches.append(self.flush_summarizer)

        # Get the updated episode and timestep counts.
        fetch_list = self.monitored_session.run(fetches=fetches)
        episode, timestep = fetch_list[:2]

        return episode, timestep, self.internals_init
def reset ( self ) : fetches = [ self . global_episode , self . global_timestep ] # Loop through all preprocessors and reset them as well. for name in sorted ( self . states_preprocessing ) : fetch = self . states_preprocessing [ name ] . reset ( ) if fetch is not None : fetches . extend ( fetch ) if self . flush_summarizer is not None : fetches . append ( self . flush_summarizer ) # Get the updated episode and timestep counts. fetch_list = self . monitored_session . run ( fetches = fetches ) episode , timestep = fetch_list [ : 2 ] return episode , timestep , self . internals_init
Resets the model to its initial state on episode start. This should also reset all preprocessor(s).

        Returns:
            tuple:
                Current episode, timestep counter and the shallow-copied list of internal state initialization Tensors.
Resets the model to its initial state on episode start . This should also reset all preprocessor ( s ) .