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前言
在深度强化学习的实验中，Atari 游戏占了很大的地位。现在我们一般使用 OpenAI 开发的 Gym 包来进行与环境的交互。本文介绍在 Atari 游戏的一些常见预处理过程。
该文所涉及到的 wrapper 均来自 OpenAI baselines
https://github.com/openai/gym…
一些常见 Wrapper 解读
Noop Reset
class NoopResetEnv(gym.Wrapper):
def __init__(self, env, noop_max=30):
"""Sample initial states by taking random number of no-ops on reset.
No-op is assumed to be action 0.
"""
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)该 wrapper 的作用是在 reset 环境的时候，使用随机数量的 no-op 动作（假设其为环境的动作 0）来采样初始化状态，如果在中途环境已经返回 done 了，则重新 reset 环境。这有利于增加初始画面的随机性，减小陷入过拟合的几率。
Fire Reset
class FireResetEnv(gym.Wrapper):
def __init__(self, env):
"""Take action on reset for environments that are fixed until firing."""
gym.Wrapper.__init__(self, env)
assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
assert len(env.unwrapped.get_action_meanings()) >= 3
def reset(self, **kwargs):
self.env.reset(**kwargs)
obs, _, done, _ = self.env.step(1)
if done:
self.env.reset(**kwargs)
obs, _, done, _ = self.env.step(2)
if done:
self.env.reset(**kwargs)
return obs
def step(self, ac):
return self.env.step(ac)
在一些 Atari 游戏中，有开火键，比如 Space Invaders，该 wrapper 的作用是返回一个选择开火动作后不 done 的环境状态。
Episodic Life
class EpisodicLifeEnv(gym.Wrapper):
def __init__(self, env):
"""Make end-of-life == end-of-episode, but only reset on true game over.
Done by DeepMind for the DQN and co. since it helps value estimation.
"""
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
# check current lives, make loss of life terminal,
# then update lives to handle bonus lives
lives = self.env.unwrapped.ale.lives()
if lives < self.lives and lives > 0:
# for Qbert sometimes we stay in lives == 0 condition for a few frames
# so it's important to keep lives > 0, so that we only reset once
# the environment advertises done.
done = True
self.lives = lives
return obs, reward, done, info
def reset(self, **kwargs):
"""Reset only when lives are exhausted.
This way all states are still reachable even though lives are episodic,
and the learner need not know about any of this behind-the-scenes.
"""
if self.was_real_done:
obs = self.env.reset(**kwargs)
else:
# no-op step to advance from terminal/lost life state
obs, _, _, _ = self.env.step(0)
self.lives = self.env.unwrapped.ale.lives()
return obs在许多游戏中，玩家操纵的角色有不止一条命，为了加速 Agent 的训练，使其尽量避免死亡，将每条命死亡后的 done 设为 True，同时使用一个属性self.was_real_done 来标记所有生命都用完之后的真正 done。
Max adn Skip
class MaxAndSkipEnv(gym.Wrapper):
def __init__(self, env, skip=4):
"""Return only every `skip`-th frame"""
gym.Wrapper.__init__(self, env)
# most recent raw observations (for max pooling across time steps)
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
# Note that the observation on the done=True frame
# doesn't matter
max_frame = self._obs_buffer.max(axis=0)
return max_frame, total_reward, done, info
def reset(self, **kwargs):
return self.env.reset(**kwargs)该 Wrapper 提供了跳帧操作，即每 skip 帧返回一次环境状态元组，在跳过的帧里执行相同的动作，将其奖励叠加，并且取最后两帧像素值中的最大值。在 Atari 游戏中，有些画面是仅在奇数帧出现的，因此要对最后两帧取最大值。
Clip Reward
class ClipRewardEnv(gym.RewardWrapper):
def __init__(self, env):
gym.RewardWrapper.__init__(self, env)
def reward(self, reward):
"""Bin reward to {+1, 0, -1} by its sign."""
return np.sign(reward)对于不同游戏来说，其得分衡量也是不同的，为了便于统一度量和学习，将所有奖励统一定义为 1（reward > 0），0（reward = 0）或 -1（reward < 0）。
Wrap Frame
class WarpFrame(gym.ObservationWrapper):
def __init__(self, env, width=84, height=84, grayscale=True, dict_space_key=None):
"""
Warp frames to 84x84 as done in the Nature paper and later work.
If the environment uses dictionary observations, `dict_space_key` can be specified which indicates which
observation should be warped.
"""
super().__init__(env)
self._width = width
self._height = height
self._grayscale = grayscale
self._key = dict_space_key
if self._grayscale:
num_colors = 1
else:
num_colors = 3
new_space = gym.spaces.Box(
low=0,
high=255,
shape=(self._height, self._width, num_colors),
dtype=np.uint8,
)
if self._key is None:
original_space = self.observation_space
self.observation_space = new_space
else:
original_space = self.observation_space.spaces[self._key]
self.observation_space.spaces[self._key] = new_space
assert original_space.dtype == np.uint8 and len(original_space.shape) == 3
def observation(self, obs):
if self._key is None:
frame = obs
else:
frame = obs[self._key]
if self._grayscale:
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
frame = cv2.resize(frame[34:194], (self._width, self._height), interpolation=cv2.INTER_AREA
)
if self._grayscale:
frame = np.expand_dims(frame, -1)
if self._key is None:
obs = frame
else:
obs = obs.copy()
obs[self._key] = frame
return obs该 Wrap 对观察到的帧的图片数据进行了处理。首先将 3 维 RGB 图像转为灰度图像，之后将其 resize 为 84 × 84 的灰度图像。本例为 Pong 游戏的 wrap Frame 处理，为使 Agent 更关注于游戏本身的画面，避免被得分等图像区域误导，我对画面进行了裁切（frame[34: 194]），对于不同的游戏，裁切的方法可能不同。
Frame Stack
class FrameStack(gym.Wrapper):
def __init__(self, env, k):
"""Stack k last frames.
Returns lazy array, which is much more memory efficient.
See Also
--------
baselines.common.atari_wrappers.LazyFrames
"""
gym.Wrapper.__init__(self, env)
self.k = k
self.frames = deque([], maxlen=k)
shp = env.observation_space.shape
self.observation_space = spaces.Box(low=0, high=255, shape=(shp[:-1] + (shp[-1] * k,)), dtype=env.observation_space.dtype)
def reset(self):
ob = self.env.reset()
for _ in range(self.k):
self.frames.append(ob)
return self._get_ob()
def step(self, action):
ob, reward, done, info = self.env.step(action)
self.frames.append(ob)
return self._get_ob(), reward, done, info
def _get_ob(self):
assert len(self.frames) == self.k
return LazyFrames(list(self.frames))本 wrapper 的作用是将 k 帧灰度图像并为一帧，以此来为 CNN 提供一些序列信息（Human Level control through deep reinforcement learning）。Wrapper 会维持一个大小为 k 的 deque，之后依次使用最新的 ob 来替代最久远的 ob，达到不同时间的状态叠加的效果。最后返回一个 LazyFrame。如果想要使用 LazyFrame，只需利用 np.array(lazy_frames_instance) 即可将 LazyFrame 对象转为 ndarray 对象。
Scaled Float Frame
class ScaledFloatFrame(gym.ObservationWrapper):
def __init__(self, env):
gym.ObservationWrapper.__init__(self, env)
self.observation_space = gym.spaces.Box(low=0, high=1, shape=env.observation_space.shape, dtype=np.float32)
def observation(self, observation):
# careful! This undoes the memory optimization, use
# with smaller replay buffers only.
return (np.array(observation).astype(np.float32) / 255.0该 Wrapper 的目的是将 0 ~ 255 的图像归一化到 [0, 1]。
后记
在强化学习训练中，环境及其预处理是一个非常重要的步骤，甚至可以直接影响到强化学习智能体的训练成功与否。除上述 Wrapper 外，读者也可另根据自己的需求来写 Wrapper，以满足需求。最后，附上 Nature DQN 的环境 Wrapper：
def wrap_deepmind(env, episode_life=True, clip_rewards=True, frame_stack=False, scale=False):
"""Configure environment for DeepMind-style Atari."""
if episode_life:
env = EpisodicLifeEnv(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = WarpFrame(env)
if scale:
env = ScaledFloatFrame(env)
if clip_rewards:
env = ClipRewardEnv(env)
if frame_stack:
env = FrameStack(env, 4)
return envPS
找了一圈博客网站，CSDN 太恶心直接拉黑，博客园又要求实名信息，总感觉不爽，最后选择了 SegmentFault。其实最终还是希望能使用自建的博客的，不过一来暂时没有主机，二来最近也没时间折腾 GitHub Pages，先在这里记录一下学习笔记好了。就酱。
正文完
