class Environment:
def __init__(self, display=Config.SHOW_MODE):
self.game = GameManager(display=display)
self.previous_state = None
self.current_state = None
self.available = None
self.total_reward = 0
self.envs_mean = None
self.envs_std = None
self.num_steps = 0
self.state_mean = 0
self.state_std = 0
self.alpha = 0.9999
self.num_steps = 0
#self.reset()
def get_num_actions(self):
return self.game.get_num_actions()
def get_num_states(self):
return self.game.get_num_state()
def _observation(self, observation):
self.num_steps += 1
self.state_mean = self.state_mean * self.alpha + \
observation.mean() * (1 - self.alpha)
self.state_std = self.state_std * self.alpha + \
observation.std() * (1 - self.alpha)
unbiased_mean = self.state_mean / (1 - pow(self.alpha, self.num_steps))
unbiased_std = self.state_std / (1 - pow(self.alpha, self.num_steps))
return (observation - unbiased_mean) / (unbiased_std + 1e-8)
def reset(self):
self.total_reward = 0
observation, available = self.game.reset()
self.previous_state = self.current_state = None
self.current_state = process_frame(observation)
return self.current_state, available
def step(self, action):
observation, reward, done, available, envs_mean, envs_std = self.game.step(
action)
self.available = available
self.total_reward += reward
self.envs_mean = envs_mean
self.envs_std = envs_std
self.previous_state = self.current_state
self.current_state = process_frame(observation)
# self.current_state = self._observation(process_frame(observation))
return self.current_state, reward, done, available, envs_mean, envs_std
class Environment:
def __init__(self):
self.game = GameManager(Config.ATARI_GAME, display=Config.PLAY_MODE)
self.nb_frames = Config.STACKED_FRAMES
self.frame_q = Queue(maxsize=self.nb_frames)
self.previous_states = []
self.current_state = None
self.total_reward = 0
self.reset()
@staticmethod
def _rgb2gray(rgb):
return np.dot(rgb[..., :3], [0.299, 0.587, 0.114])
@staticmethod
def _preprocess(image):
image = Environment._rgb2gray(image)
image = misc.imresize(image, [Config.IMAGE_HEIGHT, Config.IMAGE_WIDTH],
'bilinear')
image = image.astype(np.float32) / 128.0 - 1.0
return image
def _get_current_state(self):
if not self.frame_q.full():
return None # frame queue is not full yet.
x_ = np.array(self.frame_q.queue)
x_ = np.transpose(x_, [1, 2, 0]) # move channels
return x_
def _update_frame_q(self, frame):
if self.frame_q.full():
self.frame_q.get()
image = Environment._preprocess(frame)
self.frame_q.put(image)
def get_num_actions(self):
return self.game.get_num_actions()
def num_basic_actions(self):
return self.game.num_basic_actions()
def reset(self):
self.total_reward = 0
self.frame_q.queue.clear()
self._update_frame_q(self.game.reset())
self.previous_states = []
self.current_state = None
def step(self, actions):
observations, rewards, done, _ = self.game.step(actions)
self.total_reward += sum(rewards)
self.previous_states = [self.current_state]
#self._update_frame_q(observations[-1])
#temp = [Environment._preprocess(frame) for frame in observations[:-1]]
for i in range(len(observations) - 1):
self._update_frame_q(observations[i])
self.previous_states.append(self._get_current_state())
self._update_frame_q(observations[-1])
self.current_state = self._get_current_state()
return rewards, done
def get_num_actions(self):
return GameManager.get_num_actions()
