atari_wrappers.py

"""
This module provides wrappers of atari environments in openai gym.
Originaly from openai baselines, but modified to:
- support [dopamine](https://github.com/google/dopamine) style setting
- support StickyActions
or so.
"""
from typing import Optional

import cv2
import gym
import numpy as np
from gym import spaces

from ..prelude import Array
from ..replay import ArrayDeque

cv2.ocl.setUseOpenCL(False)


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 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 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


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 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 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, (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


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 = ArrayDeque(capacity=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.push_back(ob)
        return self._get_ob()

    def step(self, action):
        ob, reward, done, info = self.env.step(action)
        self.frames.push_back(ob)
        return self._get_ob(), reward, done, info

    def _get_ob(self):
        assert len(self.frames) == self.k
        return LazyFrames(list(self.frames))


class LazyFrames:
    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 believe how complex the previous solution was."""
        self._frames = frames
        self._out = None

    def _force(self):
        if self._out is None:
            self._out = np.concatenate(self._frames, axis=-1)
            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 __getitem__(self, i):
        return self._force()[i]

    def count(self):
        frames = self._force()
        return frames.shape[frames.ndim - 1]

    def frame(self, i):
        return self._force()[..., i]


class FlickerFrame(gym.ObservationWrapper):
    """Stochastically flicker frames."""

    def __init__(self, env: gym.Env, p: float = 0.5) -> None:
        super().__init__(env)
        self.p = p

    def observation(self, obs: Array) -> Array:
        if self.unwrapped.np_random.uniform() < self.p:
            return obs
        else:
            return np.zeros_like(obs)


class StickyActionEnv(gym.Wrapper):
    """Repeat the same action stochastically. From:
    https://github.com/openai/random-network-distillation/blob/master/atari_wrappers.py
    """

    def __init__(self, env: gym.Env, p: float = 0.25) -> None:
        super().__init__(env)
        self.p = p
        self.last_action = 0

    def reset(self) -> None:
        self.last_action = 0
        return self.env.reset()

    def step(self, action):
        if self.p <= self.unwrapped.np_random.uniform():
            self.last_action = action
        return self.env.step(self.last_action)


def make_atari(
    env_id: str,
    timelimit: bool = True,
    override_timelimit: Optional[int] = None,
    v4: bool = False,
    sticky_actions: bool = False,
    noop_reset: bool = True,
) -> gym.Env:
    version = "v0" if sticky_actions else "v4"
    env = gym.make("{}NoFrameskip-{}".format(env_id, version))
    if not timelimit:
        env = env.env
    if timelimit and override_timelimit is not None:
        env._max_episode_steps = override_timelimit
    if sticky_actions:
        env = StickyActionEnv(env)
    if noop_reset:
        env = NoopResetEnv(env, noop_max=30)
    env = MaxAndSkipEnv(env, skip=4)
    return env


def wrap_deepmind(
    env: gym.Env,
    episodic_life: bool = False,
    fire_reset: bool = False,
    clip_reward: bool = True,
    flicker_frame: bool = False,
    frame_stack: bool = True,
) -> gym.Env:
    """Configure environment for DeepMind-style Atari.
    About FireResetEnv, I recommend to see the discussion at
    https://github.com/openai/baselines/issues/240.
    """
    if episodic_life:
        env = EpisodicLifeEnv(env)
    if fire_reset and "FIRE" in env.unwrapped.get_action_meanings():
        env = FireResetEnv(env)
    env = WarpFrame(env)
    if clip_reward:
        env = ClipRewardEnv(env)
    if flicker_frame:
        env = FlickerFrame(env)
    if frame_stack:
        env = FrameStack(env, 4)
    return env