tensorforce/contrib/deepmind_lab.py
def execute(self, action):
        """
        Pass action to universe environment, return reward, next step, terminal state and
        additional info.

        :param action: action to execute as numpy array, should have dtype np.intc and should adhere to
            the specification given in DeepMindLabEnvironment.action_spec(level_id)
        :return: dict containing the next state, the reward, and a boolean indicating if the
            next state is a terminal state
        """
        adjusted_action = list()
        for action_spec in self.level.action_spec():
            if action_spec['min'] == -1 and action_spec['max'] == 1:
                adjusted_action.append(action[action_spec['name']] - 1)
            else:
                adjusted_action.append(action[action_spec['name']])  # clip?
        action = np.array(adjusted_action, dtype=np.intc)

        reward = self.level.step(action=action, num_steps=self.repeat_action)
        state = self.level.observations()['RGB_INTERLACED']
        terminal = not self.level.is_running()
        return state, terminal, reward
def execute(self, action):
        """
        Pass action to universe environment, return reward, next step, terminal state and
        additional info.

        :param action: action to execute as numpy array, should have dtype np.intc and should adhere to
            the specification given in DeepMindLabEnvironment.action_spec(level_id)
        :return: dict containing the next state, the reward, and a boolean indicating if the
            next state is a terminal state
        """
        adjusted_action = list()
        for action_spec in self.level.action_spec():
            if action_spec['min'] == -1 and action_spec['max'] == 1:
                adjusted_action.append(action[action_spec['name']] - 1)
            else:
                adjusted_action.append(action[action_spec['name']])  # clip?
        action = np.array(adjusted_action, dtype=np.intc)

        reward = self.level.step(action=action, num_steps=self.repeat_action)
        state = self.level.observations()['RGB_INTERLACED']
        terminal = not self.level.is_running()
        return state, terminal, reward
def execute ( self , action ) : adjusted_action = list ( ) for action_spec in self . level . action_spec ( ) : if action_spec [ 'min' ] == - 1 and action_spec [ 'max' ] == 1 : adjusted_action . append ( action [ action_spec [ 'name' ] ] - 1 ) else : adjusted_action . append ( action [ action_spec [ 'name' ] ] ) # clip? action = np . array ( adjusted_action , dtype = np . intc ) reward = self . level . step ( action = action , num_steps = self . repeat_action ) state = self . level . observations ( ) [ 'RGB_INTERLACED' ] terminal = not self . level . is_running ( ) return state , terminal , reward
Pass action to universe environment, return reward, next step, terminal state and
        additional info.

        :param action: action to execute as numpy array, should have dtype np.intc and should adhere to
            the specification given in DeepMindLabEnvironment.action_spec(level_id)
        :return: dict containing the next state, the reward, and a boolean indicating if the
            next state is a terminal state
Pass action to universe environment return reward next step terminal state and additional info .