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import cv2
cv2.ocl.setUseOpenCL(False)
class NoopResetEnv(gym.Wrapper):
def __init__(self, env, noop_max=30):
'''工夫1のNo-Operationです。リセット後適当なステップの間何もしないようにし、
ゲーム開始の初期状態を様々にすることｆで、特定の開始状態のみで学習するのを防ぐ'''
gym.Wrapper.__init__(self, env)
self.noop_max = noop_max
self.override_num_noops = None
self.noop_action = 0
assert env.unwrapped.get_action_meanings()[0] == 'NOOP'
def reset(self, **kwargs):
""" Do no-op action for a number of steps in [1, noop_max]."""
self.env.reset(**kwargs)
if self.override_num_noops is not None:
noops = self.override_num_noops
else:
noops = self.unwrapped.np_random.randint(
1, self.noop_max + 1)
assert noops > 0
obs = None
for _ in range(noops):
obs, _, done, _ = self.env.step(self.noop_action)
if done:
obs = self.env.reset(**kwargs)
return obs
def step(self, ac):
return self.env.step(ac)
class EpisodicLifeEnv(gym.Wrapper):
def __init__(self, env):
'''工夫2のEpisodic Lifeです。1機失敗したときにリセットし、失敗時の状態から次を始める'''
gym.Wrapper.__init__(self, env)
self.lives = 0
self.was_real_done = True
def step(self, action):
obs, reward, done, info = self.env.step(action)
self.was_real_done = done
lives = self.env.unwrapped.ale.lives()
if lives < self.lives and lives > 0:
done = True
self.lives = lives
return obs, reward, done, info
def reset(self, **kwargs):
'''5機とも失敗したら、本当にリセット'''
if self.was_real_done:
obs = self.env.reset(**kwargs)
else:
obs, _, _, _ = self.env.step(0)
self.lives = self.env.unwrapped.ale.lives()
return obs
class MaxAndSkipEnv(gym.Wrapper):
def __init__(self, env, skip=4):
'''工夫3のMax and Skipです。4フレーム連続で同じ行動を実施し、最後の3、4フレームの最大値をとった画像をobsにする'''
gym.Wrapper.__init__(self, env)
self._obs_buffer = np.zeros(
(2,)+env.observation_space.shape, dtype=np.uint8)
self._skip = skip
def step(self, action):
"""Repeat action, sum reward, and max over last observations."""
total_reward = 0.0
done = None
for i in range(self._skip):
obs, reward, done, info = self.env.step(action)
if i == self._skip - 2:
self._obs_buffer[0] = obs
if i == self._skip - 1:
self._obs_buffer[1] = obs
total_reward += reward
if done:
break
max_frame = self._obs_buffer.max(axis=0)
return max_frame, total_reward, done, info
def reset(self, **kwargs):
return self.env.reset(**kwargs)
class WarpFrame(gym.ObservationWrapper):
def __init__(self, env):
'''工夫4のWarp frameです。画像サイズをNatureのDQN論文と同じ84x84の白黒にします'''
gym.ObservationWrapper.__init__(self, env)
self.width = 84
self.height = 84
self.observation_space = spaces.Box(low=0, high=255, shape=(self.height, self.width, 1), dtype=np.uint8)
def observation(self, frame):
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
return frame[:, :, None]
class WrapPyTorch(gym.ObservationWrapper):
def __init__(self, env=None):
'''PyTorchのミニバッチのインデックス順に変更するラッパー'''
super(WrapPyTorch, self).__init__(env)
obs_shape = self.observation_space.shape
self.observation_space = Box(
self.observation_space.low[0, 0, 0],
self.observation_space.high[0, 0, 0],
[obs_shape[2], obs_shape[1], obs_shape[0]],
dtype=self.observation_space.dtype)
def observation(self, observation):
return observation.transpose(2, 0, 1)
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