slm_lab/env/wrapper.py
# Generic env wrappers, including for Atari/images
# They don't come with Gym but are crucial for Atari to work
# Many were adapted from OpenAI Baselines (MIT) https://github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py
from collections import deque
from gym import spaces
from slm_lab.lib import util
import gym
import numpy as np
def try_scale_reward(cls, reward):
'''Env class to scale reward'''
if util.in_eval_lab_mode(): # only trigger on training
return reward
if cls.reward_scale is not None:
if cls.sign_reward:
reward = np.sign(reward)
else:
reward *= cls.reward_scale
return reward
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)
class MaxAndSkipEnv(gym.Wrapper):
'''OpenAI max-skipframe wrapper used for a NoFrameskip env'''
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)
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 = info['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 condtion for a few frames
# so its 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
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)
class PreprocessImage(gym.ObservationWrapper):
def __init__(self, env, w_h):
'''
Apply image preprocessing:
- grayscale
- downsize to 84x84
- transpose shape from h,w,c to PyTorch format c,h,w
'''
gym.ObservationWrapper.__init__(self, env)
w_h = w_h or (84, 84)
self.width, self.height = w_h
self.observation_space = spaces.Box(
low=0, high=255, shape=(1, self.width, self.height), dtype=np.uint8)
def observation(self, frame):
return util.preprocess_image(frame, (self.width, self.height))
class LazyFrames(object):
def __init__(self, frames, frame_op='stack'):
'''
Wrapper to stack or concat frames by keeping unique soft reference insted of copies of data.
So this should only be converted to numpy array before being passed to the model.
It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay buffers.
@param str:frame_op 'stack' or 'concat'
'''
self._frames = frames
self._out = None
if frame_op == 'stack':
self._frame_op = np.stack
elif frame_op == 'concat':
self._frame_op = np.concatenate
else:
raise ValueError('frame_op not recognized for LazyFrames. Choose from "stack", "concat"')
def _force(self):
if self._out is None:
self._out = self._frame_op(self._frames, axis=0)
self._frames = None
return self._out
def __array__(self, dtype=None):
out = self._force()
if dtype is not None:
out = out.astype(dtype)
return out
def __len__(self):
return len(self._force())
def __getitem__(self, i):
return self._force()[i]
def astype(self, dtype):
'''To prevent state.astype(np.float16) breaking on LazyFrames'''
return self
class FrameStack(gym.Wrapper):
def __init__(self, env, frame_op, frame_op_len):
'''
Stack/concat last k frames. Returns lazy array, which is much more memory efficient.
@param str:frame_op 'concat' or 'stack'. Note: use concat for image since the shape is (1, 84, 84) concat-able.
@param int:frame_op_len The number of frames to keep for frame_op
'''
gym.Wrapper.__init__(self, env)
self.frame_op = frame_op
self.frame_op_len = frame_op_len
self.frames = deque([], maxlen=self.frame_op_len)
old_shape = env.observation_space.shape
if self.frame_op == 'concat': # concat multiplies first dim
shape = (self.frame_op_len * old_shape[0],) + old_shape[1:]
elif self.frame_op == 'stack': # stack creates new dim
shape = (self.frame_op_len,) + old_shape
else:
raise ValueError('frame_op not recognized for FrameStack. Choose from "stack", "concat".')
self.observation_space = spaces.Box(
low=np.min(env.observation_space.low),
high=np.max(env.observation_space.high),
shape=shape, dtype=env.observation_space.dtype)
def reset(self):
ob = self.env.reset()
for _ in range(self.frame_op_len):
self.frames.append(ob.astype(np.float16))
return self._get_ob()
def step(self, action):
ob, reward, done, info = self.env.step(action)
self.frames.append(ob.astype(np.float16))
return self._get_ob(), reward, done, info
def _get_ob(self):
assert len(self.frames) == self.frame_op_len
return LazyFrames(list(self.frames), self.frame_op)
class UnityVecFrameStack(gym.Wrapper):
'''Frame stack wrapper for Unity vector environment'''
def __init__(self, env, frame_op, frame_op_len):
self.env = env
assert frame_op in ('concat', 'stack'), 'Invalid frame_op mode'
self.is_stack = frame_op == 'stack'
self.frame_op_len = frame_op_len
self.spec = env.spec
wos = env.observation_space # wrapped ob space
if self.is_stack:
self.shape_dim0 = 1
low = np.repeat(np.expand_dims(wos.low, axis=0), self.frame_op_len, axis=0)
high = np.repeat(np.expand_dims(wos.high, axis=0), self.frame_op_len, axis=0)
else: # concat
self.shape_dim0 = wos.shape[0]
low = np.repeat(wos.low, self.frame_op_len, axis=0)
high = np.repeat(wos.high, self.frame_op_len, axis=0)
self.stackedobs = np.zeros((env.num_envs,) + low.shape, low.dtype)
self.observation_space = spaces.Box(low=low, high=high, dtype=env.observation_space.dtype)
self.action_space = env.action_space
def step(self, action):
obs, rews, news, infos = self.env.step(action)
self.stackedobs[:, :-self.shape_dim0] = self.stackedobs[:, self.shape_dim0:]
for (i, new) in enumerate(news):
if new:
self.stackedobs[i] = 0
if self.is_stack:
obs = np.expand_dims(obs, axis=1)
self.stackedobs[:, -self.shape_dim0:] = obs
return self.stackedobs.copy(), rews, news, infos
def reset(self):
obs = self.env.reset()
self.stackedobs[...] = 0
if self.is_stack:
obs = np.expand_dims(obs, axis=1)
self.stackedobs[:, -self.shape_dim0:] = obs
return self.stackedobs.copy()
class NormalizeStateEnv(gym.ObservationWrapper):
def __init__(self, env=None):
'''
Normalize observations on-line
Adapted from https://github.com/ikostrikov/pytorch-a3c/blob/e898f7514a03de73a2bf01e7b0f17a6f93963389/envs.py (MIT)
'''
super().__init__(env)
self.state_mean = 0
self.state_std = 0
self.alpha = 0.9999
self.num_steps = 0
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)
class ScaleRewardEnv(gym.RewardWrapper):
def __init__(self, env, reward_scale):
'''
Rescale reward
@param (str,float):reward_scale If 'sign', use np.sign, else multiply with the specified float scale
'''
gym.Wrapper.__init__(self, env)
self.reward_scale = reward_scale
self.sign_reward = self.reward_scale == 'sign'
def reward(self, reward):
return try_scale_reward(self, reward)
class TrackReward(gym.Wrapper):
def __init__(self, env):
'''
Self-tracking as a simple solution to total reward tracking
Tracks the latest episodic rewards
'''
gym.Wrapper.__init__(self, env)
self.tracked_reward = 0
self.total_reward = np.nan
def step(self, action):
obs, reward, done, info = self.env.step(action)
self.tracked_reward += reward
# fix shape by inferring from reward
if np.isscalar(self.total_reward) and not np.isscalar(reward):
self.total_reward = np.full_like(reward, self.total_reward)
# use self.was_real_done from EpisodicLifeEnv, or plain done
real_done = info.get('was_real_done', False) or done
not_real_done = (1 - real_done)
# if isnan and at done, reset total_reward from nan to 0 so it can be updated with tracked_reward
if np.isnan(self.total_reward).any():
if np.isscalar(self.total_reward):
if np.isnan(self.total_reward) and real_done:
self.total_reward = 0.0
else:
replace_locs = np.logical_and(np.isnan(self.total_reward), real_done)
self.total_reward[replace_locs] = 0.0
# update total_reward
self.total_reward = self.total_reward * not_real_done + self.tracked_reward * real_done
# reset to 0 on real_done, i.e. multiply with not_real_done
self.tracked_reward = self.tracked_reward * not_real_done
info.update({'total_reward': self.total_reward})
return obs, reward, done, info
def reset(self, **kwargs):
self.tracked_reward = 0
return self.env.reset(**kwargs)
def wrap_atari(env):
'''Apply a common set of wrappers for Atari games'''
assert 'NoFrameskip' in env.spec.id
env = NoopResetEnv(env, noop_max=30)
env = MaxAndSkipEnv(env, skip=4)
return env
def wrap_deepmind(env, episode_life=True, stack_len=None, image_downsize=None):
'''Wrap Atari environment DeepMind-style'''
if episode_life:
env = EpisodicLifeEnv(env)
if 'FIRE' in env.unwrapped.get_action_meanings():
env = FireResetEnv(env)
env = PreprocessImage(env, image_downsize)
if stack_len is not None: # use concat for image (1, 84, 84)
env = FrameStack(env, 'concat', stack_len)
return env
def make_gym_env(name, seed=None, frame_op=None, frame_op_len=None, image_downsize=None, reward_scale=None, normalize_state=False, episode_life=True):
'''General method to create any Gym env; auto wraps Atari'''
env = gym.make(name)
if seed is not None:
env.seed(seed)
if 'NoFrameskip' in env.spec.id: # Atari
env = wrap_atari(env)
# no reward clipping to allow monitoring; Atari memory clips it
env = wrap_deepmind(env, episode_life, frame_op_len, image_downsize)
elif len(env.observation_space.shape) == 3: # image-state env
env = PreprocessImage(env, image_downsize)
if normalize_state:
env = NormalizeStateEnv(env)
if frame_op_len is not None: # use concat for image (1, 84, 84)
env = FrameStack(env, 'concat', frame_op_len)
else: # vector-state env
if normalize_state:
env = NormalizeStateEnv(env)
if frame_op is not None:
Stacker = UnityVecFrameStack if name.startswith('Unity') else FrameStack
env = Stacker(env, frame_op, frame_op_len)
env = TrackReward(env) # auto-track total reward
if reward_scale is not None:
env = ScaleRewardEnv(env, reward_scale)
return env