ray/python/ray/rllib/dqn/common/atari_wrappers_deprecated.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import cv2
import gym
import numpy as np

from collections import deque
from gym import spaces


class NoopResetEnv(gym.Wrapper):
  def __init__(self, env=None, noop_max=30):
    """Sample initial states by taking random number of no-ops on reset.
    No-op is assumed to be action 0.
    """
    super(NoopResetEnv, self).__init__(env)
    self.noop_max = noop_max
    self.override_num_noops = None
    assert env.unwrapped.get_action_meanings()[0] == 'NOOP'

  def _reset(self):
    """ Do no-op action for a number of steps in [1, noop_max]."""
    self.env.reset()
    if self.override_num_noops is not None:
      noops = self.override_num_noops
    else:
      noops = np.random.randint(1, self.noop_max + 1)
    assert noops > 0
    obs = None
    for _ in range(noops):
      obs, _, done, _ = self.env.step(0)
      if done:
        obs = self.env.reset()
    return obs


class FireResetEnv(gym.Wrapper):
  def __init__(self, env=None):
    """For environments where the user need to press FIRE for the game to
    start."""
    super(FireResetEnv, self).__init__(env)
    assert env.unwrapped.get_action_meanings()[1] == 'FIRE'
    assert len(env.unwrapped.get_action_meanings()) >= 3

  def _reset(self):
    self.env.reset()
    obs, _, done, _ = self.env.step(1)
    if done:
      self.env.reset()
    obs, _, done, _ = self.env.step(2)
    if done:
      self.env.reset()
    return obs


class EpisodicLifeEnv(gym.Wrapper):
  def __init__(self, env=None):
    """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.
    """
    super(EpisodicLifeEnv, self).__init__(env)
    self.lives = 0
    self.was_real_done = True
    self.was_real_reset = False

  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):
    """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()
      self.was_real_reset = True
    else:
      # no-op step to advance from terminal/lost life state
      obs, _, _, _ = self.env.step(0)
      self.was_real_reset = False
    self.lives = self.env.unwrapped.ale.lives()
    return obs


class MaxAndSkipEnv(gym.Wrapper):
  def __init__(self, env=None, skip=4):
    """Return only every `skip`-th frame"""
    super(MaxAndSkipEnv, self).__init__(env)
    # most recent raw observations (for max pooling across time steps)
    self._obs_buffer = deque(maxlen=2)
    self._skip = skip

  def _step(self, action):
    total_reward = 0.0
    done = None
    for _ in range(self._skip):
      obs, reward, done, info = self.env.step(action)
      self._obs_buffer.append(obs)
      total_reward += reward
      if done:
        break

    max_frame = np.max(np.stack(self._obs_buffer), axis=0)

    return max_frame, total_reward, done, info

  def _reset(self):
    """Clear past frame buffer and init. to first obs. from inner env."""
    self._obs_buffer.clear()
    obs = self.env.reset()
    self._obs_buffer.append(obs)
    return obs


class ProcessFrame84(gym.ObservationWrapper):
  def __init__(self, env=None):
    super(ProcessFrame84, self).__init__(env)
    self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1))

  def _observation(self, obs):
    return ProcessFrame84.process(obs)

  @staticmethod
  def process(frame):
    if frame.size == 210 * 160 * 3:
      img = np.reshape(frame, [210, 160, 3]).astype(np.float32)
    elif frame.size == 250 * 160 * 3:
      img = np.reshape(frame, [250, 160, 3]).astype(np.float32)
    else:
      assert False, "Unknown resolution."
    img = img[:, :, 0] * 0.299 + img[:, :, 1] * 0.587 + img[:, :, 2] * 0.114
    resized_screen = cv2.resize(img, (84, 110), interpolation=cv2.INTER_AREA)
    x_t = resized_screen[18:102, :]
    x_t = np.reshape(x_t, [84, 84, 1])
    return x_t.astype(np.uint8)


class ClippedRewardsWrapper(gym.RewardWrapper):
  def _reward(self, reward):
    """Change all the positive rewards to 1, negative to -1 and keep zero."""
    return np.sign(reward)


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


class FrameStack(gym.Wrapper):
  def __init__(self, env, k):
    """Stack k last frames.

    Returns lazy array, which is much more memory efficient.

    See Also
    --------
    ray.rllib.dqn.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, obs):
    # careful! This undoes the memory optimization, use
    # with smaller replay buffers only.
    return np.array(obs).astype(np.float32) / 255.0


def wrap_dqn(env):
  """Apply a common set of wrappers for Atari games."""
  assert 'NoFrameskip' in env.spec.id
  env = EpisodicLifeEnv(env)
  env = NoopResetEnv(env, noop_max=30)
  env = MaxAndSkipEnv(env, skip=4)
  if 'FIRE' in env.unwrapped.get_action_meanings():
    env = FireResetEnv(env)
  env = ProcessFrame84(env)
  env = FrameStack(env, 4)
  env = ClippedRewardsWrapper(env)
  return env


class A2cProcessFrame(gym.Wrapper):
  def __init__(self, env):
    gym.Wrapper.__init__(self, env)
    self.observation_space = spaces.Box(low=0, high=255, shape=(84, 84, 1))

  def _step(self, action):
    ob, reward, done, info = self.env.step(action)
    return A2cProcessFrame.process(ob), reward, done, info

  def _reset(self):
    return A2cProcessFrame.process(self.env.reset())

  @staticmethod
  def process(frame):
    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
    frame = cv2.resize(frame, (84, 84), interpolation=cv2.INTER_AREA)
    return frame.reshape(84, 84, 1)