ray/python/ray/rllib/env/atari_wrappers.py

import numpy as np
from collections import deque
import gym
from gym import spaces
import cv2
cv2.ocl.setUseOpenCL(False)


def is_atari(env):
    return hasattr(env, "unwrapped") and hasattr(env.unwrapped, "ale")


class NoopResetEnv(gym.Wrapper):
    def __init__(self, env, noop_max=30, random_starts=False):
        """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
        self.random_starts = random_starts
        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):
            if self.random_starts:
                action = np.random.randint(self.env.action_space.n)
            else:
                action = self.noop_action
            obs, _, done, _ = self.env.step(action)
            if done:
                obs = self.env.reset(**kwargs)
        return obs

    def step(self, ac):
        return self.env.step(ac)


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)


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 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 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=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 WarpFrame(gym.ObservationWrapper):
    def __init__(self, env, dim):
        """Warp frames to the specified size (dim x dim)."""
        gym.ObservationWrapper.__init__(self, env)
        self.width = dim  # in rllib we use 80
        self.height = dim
        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."""
        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 np.concatenate(self.frames, axis=2)


def wrap_deepmind(env, random_starts=True, dim=80):
    """Configure environment for DeepMind-style Atari.

    Note that we assume reward clipping is done outside the wrapper.

    Args:
        random_starts (bool): Start with random actions instead of noops.
        dim (int): Dimension to resize observations to (dim x dim).
    """
    env = NoopResetEnv(env, noop_max=30, random_starts=random_starts)
    if 'NoFrameskip' in env.spec.id:
        env = MaxAndSkipEnv(env, skip=4)
    env = EpisodicLifeEnv(env)
    if 'FIRE' in env.unwrapped.get_action_meanings():
        env = FireResetEnv(env)
    env = WarpFrame(env, dim)
    # env = ClipRewardEnv(env)  # reward clipping is handled by DQN replay
    env = FrameStack(env, 4)
    return env