agent.py
def act_and_perceive(self, action_selected, percept_stack):
        """
        Acts in the environment. That is, it moves the game
        one step further by passing the selected action,
        it then reads what the environment had to say about that
        and finally preprocesses the image into the format and
        size used by the network and appends the new image into the front
        of the image stack
        """
        log.debug('acting with ' + str(action_selected))
        new_percept, reward = self.environment.frame_step(action_selected)
        log.debug('got reward of ' + str(reward))

        new_percept = self.perception.preprocess_percepts(new_percept)
        log.debug('got the new image')

        new_percept_stack = np.append(new_percept, percept_stack[:, :, :3], axis=2)
        log.debug('modified the stack to include the new image')
        return new_percept_stack, reward

        Acts in the environment. That is, it moves the game
        one step further by passing the selected action,
        it then reads what the environment had to say about that
        and finally preprocesses the image into the format and
        size used by the network and appends the new image into the front
        of the image stack
        
Acts in the environment. That is, it moves the game
one step further by passing the selected action,
it then reads what the environment had to say about that
and finally preprocesses the image into the format and
size used by the network and appends the new image into the front
of the image stack
Acts in the environment . That is it moves the game one step further by passing the selected action it then reads what the environment had to say about that and finally preprocesses the image into the format and size used by the network and appends the new image into the front of the image stack
def act_and_perceive(self, action_selected, percept_stack):
        log.debug('acting with ' + str(action_selected))
        new_percept, reward = self.environment.frame_step(action_selected)
        log.debug('got reward of ' + str(reward))
        new_percept = self.perception.preprocess_percepts(new_percept)
        log.debug('got the new image')
        new_percept_stack = np.append(new_percept, percept_stack[:, :, :3], axis=2)
        log.debug('modified the stack to include the new image')
        return new_percept_stack, reward
def act_and_perceive ( self , action_selected , percept_stack ) : log . debug ( 'acting with ' + str ( action_selected ) ) new_percept , reward = self . environment . frame_step ( action_selected ) log . debug ( 'got reward of ' + str ( reward ) ) new_percept = self . perception . preprocess_percepts ( new_percept ) log . debug ( 'got the new image' ) new_percept_stack = np . append ( new_percept , percept_stack [ : , : , : 3 ] , axis = 2 ) log . debug ( 'modified the stack to include the new image' ) return new_percept_stack , reward
Acts in the environment.
Acts in the environment .
"""
        Acts in the environment. That is, it moves the game
        one step further by passing the selected action,
        it then reads what the environment had to say about that
        and finally preprocesses the image into the format and
        size used by the network and appends the new image into the front
        of the image stack
        """
self
action_selected
percept_stack