wrappers.py

"""
Basic wrappers, useful for reinforcement learning on gym envs
https://github.com/hill-a/stable-baselines/blob/master/stable_baselines/common/atari_wrappers.py
"""

from collections import deque

import cv2
import gym
import numpy as np
from gym import spaces
from nes_py.wrappers import JoypadSpace

cv2.ocl.setUseOpenCL(False)


class FireResetEnv(gym.Wrapper):
    def __init__(self, env):
        """
        Take action on reset for environments that are fixed until firing.

        :param env: (Gym Environment) the environment to wrap
        """
        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, action):
        return self.env.step(action)


class MaxAndSkipEnv(gym.Wrapper):
    def __init__(self, env, skip=4):
        """
        Return only every `skip`-th frame (frameskipping)

        :param env: (Gym Environment) the environment
        :param skip: (int) number of `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=env.observation_space.dtype)
        self._skip = skip

    def step(self, action):
        """
        Step the environment with the given action
        Repeat action, sum reward, and max over last observations.

        :param action: ([int] or [float]) the action
        :return: ([int] or [float], [float], [bool], dict) observation, reward, done, information
        """
        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 FrameDownSample(gym.ObservationWrapper):
    def __init__(self, env):
        """
        Down sample frames to 84x84 as done in the Nature paper and later work.

        :param env: (Gym Environment) the environment
        """
        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),
                                            dtype=env.observation_space.dtype)

    def observation(self, frame):
        """
        returns the current observation from a frame

        :param frame: ([int] or [float]) environment frame
        :return: ([int] or [float]) the observation
        """
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
        frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
        return frame[:, :, None]


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

        Returns lazy array, which is much more memory efficient.

        See Also
        --------
        stable_baselines.common.atari_wrappers.LazyFrames
        NOTE: Changed high from 255 to 1.0 to match with FrameDownSample observation_space

        :param env: (Gym Environment) the environment
        :param n_frames: (int) the number of frames to stack
        """
        gym.Wrapper.__init__(self, env)
        self.n_frames = n_frames
        self.frames = deque([], maxlen=n_frames)
        shp = env.observation_space.shape
        self.observation_space = spaces.Box(low=0, high=1.0, shape=(shp[0], shp[1], shp[2] * n_frames),
                                            dtype=env.observation_space.dtype)

    def reset(self, **kwargs):
        obs = self.env.reset(**kwargs)
        for _ in range(self.n_frames):
            self.frames.append(obs)
        return self._get_ob()

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

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


class ScaledFloatFrame(gym.ObservationWrapper):
    def __init__(self, env):
        gym.ObservationWrapper.__init__(self, env)
        self.observation_space = spaces.Box(low=0, high=1.0, shape=env.observation_space.shape, dtype=np.float32)

    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 np.ndarray before being passed to the model.

        :param frames: ([int] or [float]) environment frames
        """
        self._frames = frames
        self._out = None

    def _force(self):
        if self._out is None:
            self._out = np.concatenate(self._frames, axis=2)
            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 __len__(self):
        return len(self._force())

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


class CustomReward(gym.Wrapper):
    def __init__(self, env):
        gym.Wrapper.__init__(self, env)
        self._current_score = 0

    def step(self, action):
        state, reward, done, info = self.env.step(action)
        reward += (info['score'] - self._current_score) / 40.0
        self._current_score = info['score']
        if done:
            if info['flag_get']:
                reward += 350.0
            else:
                reward -= 50.0
        return state, reward / 10.0, done, info


def wrap_nes(env_id, action_space):
    """
    Configure environment for NES.

    :param env_id: (str) the environment ID
    :param action_space: (list) action space
    :return: (Gym Environment) the wrapped environment
    """
    env = gym.make(env_id)
    env = JoypadSpace(env, action_space)
    env = MaxAndSkipEnv(env)
    if 'FIRE' in env.unwrapped.get_action_meanings():
        env = FireResetEnv(env)
    env = FrameDownSample(env)
    env = ScaledFloatFrame(env)
    env = LazyFrameStack(env, 4)
    env = CustomReward(env)
    return env