Run one timestep of the environment's dynamics. When end of
        episode is reached, you are responsible for calling `reset()`
        to reset this environment's state.
        Accepts an action and returns a tuple (observation, reward, done, info).
        In the case of multi-agent environments, these are lists.
        Args:
            action (object/list): an action provided by the environment
        Returns:
            observation (object/list): agent's observation of the current environment
            reward (float/list) : amount of reward returned after previous action
            done (boolean/list): whether the episode has ended.
            info (dict): contains auxiliary diagnostic information, including BrainInfo.

def step(self, action):
        """Run one timestep of the environment's dynamics. When end of
        episode is reached, you are responsible for calling `reset()`
        to reset this environment's state.
        Accepts an action and returns a tuple (observation, reward, done, info).
        In the case of multi-agent environments, these are lists.
        Args:
            action (object/list): an action provided by the environment
        Returns:
            observation (object/list): agent's observation of the current environment
            reward (float/list) : amount of reward returned after previous action
            done (boolean/list): whether the episode has ended.
            info (dict): contains auxiliary diagnostic information, including BrainInfo.
        """

        # Use random actions for all other agents in environment.
        if self._multiagent:
            if not isinstance(action, list):
                raise UnityGymException("The environment was expecting `action` to be a list.")
            if len(action) != self._n_agents:
                raise UnityGymException(
                    "The environment was expecting a list of {} actions.".format(self._n_agents))
            else:
                if self._flattener is not None:
                    # Action space is discrete and flattened - we expect a list of scalars
                    action = [self._flattener.lookup_action(_act) for _act in action]
                action = np.array(action)
        else:
            if self._flattener is not None:
                # Translate action into list
                action = self._flattener.lookup_action(action)

        info = self._env.step(action)[self.brain_name]
        n_agents = len(info.agents)
        self._check_agents(n_agents)
        self._current_state = info

        if not self._multiagent:
            obs, reward, done, info = self._single_step(info)
            self.game_over = done
        else:
            obs, reward, done, info = self._multi_step(info)
            self.game_over = all(done)
        return obs, reward, done, info