atari_wrapper.py

# -*- coding: utf-8 -*- #
"""*********************************************************************************************"""
#   FileName     [ atari wrapper.py ]
#   Synopsis     [ environment wrapper for atari ]
#   Author       [ Ting-Wei Liu (Andi611) ]
#   Copyright    [ Copyleft(c), NTUEE, NTU, Taiwan ]
#   Reference    [ github.com/openai/baselines/blob/master/baselines/common/atari_wrappers.py ]
"""*********************************************************************************************"""


###############
# IMPORTATION #
###############
import cv2
import gym
import numpy as np
from gym import spaces
from collections import deque


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
		if isinstance(env.action_space, gym.spaces.MultiBinary):
			self.noop_action = np.zeros(self.env.action_space.n, dtype=np.int64)
		else:
			# used for atari environments
			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) #pylint: disable=E1101
		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


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


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 somtimes 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 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='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


class ClipRewardEnv(gym.RewardWrapper):
	def _reward(self, reward):
		"""Bin reward to {+1, 0, -1} by its sign."""
		return np.sign(reward)


class WarpFrame(gym.ObservationWrapper):
	def __init__(self, env):
		"""Warp frames to 84x84 as done in the Nature paper and later work."""
		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))

	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 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[0], shp[1], shp[2] * k))

	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))


class ScaledFloatFrame(gym.ObservationWrapper):
	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


class LazyFrames(object):
	def __init__(self, frames):
		"""This object ensures that common frames between the observations are only stored once.
		It exists purely to optimize memory usage which can be huge for DQN's 1M frames replay
		buffers.
		This object should only be converted to numpy array before being passed to the model.
		You'd not belive how complex the previous solution was."""
		self._frames = frames

	def __array__(self, dtype=None):
		out = np.concatenate(self._frames, axis=2)
		if dtype is not None:
			out = out.astype(dtype)
		return out


def make_atari(env_id):
	env = gym.make(env_id)
	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, 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 env


def make_wrap_atari(env_id='Breakout-v0', clip_rewards=True):
	#env = gym.make(env_id)
	env = make_atari(env_id)
	return wrap_deepmind(env, clip_rewards=clip_rewards, frame_stack=True, scale=True)