basic_utils/env_wrapper.py
def step(self, action):
        """
        Take one step in the environment.

        Args:
            action: the action to take

        Return:
            history_normalized: the concatenated and normalized observations
            r: reward
            done: whether the game is over
            info: a dict containing the raw observations and raw rewards.
        """
        ob, r, done, info = self.env.step(action)
        ob = self._process(ob)
        self.states.append(ob)
        history = np.concatenate(self.states, axis=-1)
        history = np.transpose(history, (2, 0, 1))
        history_normalized = self._normalize_ob(history)
        info["reward_raw"] = r
        info["observation_raw"] = history
        return history_normalized, r, done, info

        Take one step in the environment.

        Args:
            action: the action to take

        Return:
            history_normalized: the concatenated and normalized observations
            r: reward
            done: whether the game is over
            info: a dict containing the raw observations and raw rewards.
        
Take one step in the environment.
Take one step in the environment .
def step(self, action):
        ob, r, done, info = self.env.step(action)
        ob = self._process(ob)
        self.states.append(ob)
        history = np.concatenate(self.states, axis=-1)
        history = np.transpose(history, (2, 0, 1))
        history_normalized = self._normalize_ob(history)
        info["reward_raw"] = r
        info["observation_raw"] = history
        return history_normalized, r, done, info
def step ( self , action ) : ob , r , done , info = self . env . step ( action ) ob = self . _process ( ob ) self . states . append ( ob ) history = np . concatenate ( self . states , axis = - 1 ) history = np . transpose ( history , ( 2 , 0 , 1 ) ) history_normalized = self . _normalize_ob ( history ) info [ "reward_raw" ] = r info [ "observation_raw" ] = history return history_normalized , r , done , info
Take one step in the environment.
Take one step in the environment .
"""
        Take one step in the environment.

        Args:
            action: the action to take

        Return:
            history_normalized: the concatenated and normalized observations
            r: reward
            done: whether the game is over
            info: a dict containing the raw observations and raw rewards.
        """
self
action
the action to take
the action to take