atari_wrappers.py

'''
Atari wrappers from https://github.com/openai/baselines/
'''
import cv2
import gym
import numpy as np

from collections import deque
from gym import spaces


def wrap_dqn(env):
    """Apply a common set of wrappers for Atari games."""
    if 'FIRE' in env.unwrapped.get_action_meanings():
        env = FireResetEnv(env)
    env = ProcessFrame84(env)
    env = FrameStack(env, 4)
    return env

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 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(img):
        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, [1,84, 84])
        return x_t.astype(np.uint8)



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=0)
        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
        --------
        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))