Reinforced-Learning-Godot/rl/Lib/site-packages/stable_baselines3/common/atari_wrappers.py

from typing import Dict, SupportsFloat

import gymnasium as gym
import numpy as np
from gymnasium import spaces

from stable_baselines3.common.type_aliases import AtariResetReturn, AtariStepReturn

try:
    import cv2

    cv2.ocl.setUseOpenCL(False)
except ImportError:
    cv2 = None  # type: ignore[assignment]


class StickyActionEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
    """
    Sticky action.

    Paper: https://arxiv.org/abs/1709.06009
    Official implementation: https://github.com/mgbellemare/Arcade-Learning-Environment

    :param env: Environment to wrap
    :param action_repeat_probability: Probability of repeating the last action
    """

    def __init__(self, env: gym.Env, action_repeat_probability: float) -> None:
        super().__init__(env)
        self.action_repeat_probability = action_repeat_probability
        assert env.unwrapped.get_action_meanings()[0] == "NOOP"  # type: ignore[attr-defined]

    def reset(self, **kwargs) -> AtariResetReturn:
        self._sticky_action = 0  # NOOP
        return self.env.reset(**kwargs)

    def step(self, action: int) -> AtariStepReturn:
        if self.np_random.random() >= self.action_repeat_probability:
            self._sticky_action = action
        return self.env.step(self._sticky_action)


class NoopResetEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
    """
    Sample initial states by taking random number of no-ops on reset.
    No-op is assumed to be action 0.

    :param env: Environment to wrap
    :param noop_max: Maximum value of no-ops to run
    """

    def __init__(self, env: gym.Env, noop_max: int = 30) -> None:
        super().__init__(env)
        self.noop_max = noop_max
        self.override_num_noops = None
        self.noop_action = 0
        assert env.unwrapped.get_action_meanings()[0] == "NOOP"  # type: ignore[attr-defined]

    def reset(self, **kwargs) -> AtariResetReturn:
        self.env.reset(**kwargs)
        if self.override_num_noops is not None:
            noops = self.override_num_noops
        else:
            noops = self.unwrapped.np_random.integers(1, self.noop_max + 1)
        assert noops > 0
        obs = np.zeros(0)
        info: Dict = {}
        for _ in range(noops):
            obs, _, terminated, truncated, info = self.env.step(self.noop_action)
            if terminated or truncated:
                obs, info = self.env.reset(**kwargs)
        return obs, info


class FireResetEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
    """
    Take action on reset for environments that are fixed until firing.

    :param env: Environment to wrap
    """

    def __init__(self, env: gym.Env) -> None:
        super().__init__(env)
        assert env.unwrapped.get_action_meanings()[1] == "FIRE"  # type: ignore[attr-defined]
        assert len(env.unwrapped.get_action_meanings()) >= 3  # type: ignore[attr-defined]

    def reset(self, **kwargs) -> AtariResetReturn:
        self.env.reset(**kwargs)
        obs, _, terminated, truncated, _ = self.env.step(1)
        if terminated or truncated:
            self.env.reset(**kwargs)
        obs, _, terminated, truncated, _ = self.env.step(2)
        if terminated or truncated:
            self.env.reset(**kwargs)
        return obs, {}


class EpisodicLifeEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
    """
    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.

    :param env: Environment to wrap
    """

    def __init__(self, env: gym.Env) -> None:
        super().__init__(env)
        self.lives = 0
        self.was_real_done = True

    def step(self, action: int) -> AtariStepReturn:
        obs, reward, terminated, truncated, info = self.env.step(action)
        self.was_real_done = terminated or truncated
        # check current lives, make loss of life terminal,
        # then update lives to handle bonus lives
        lives = self.env.unwrapped.ale.lives()  # type: ignore[attr-defined]
        if 0 < lives < self.lives:
            # for Qbert sometimes we stay in lives == 0 condition for a few frames
            # so its important to keep lives > 0, so that we only reset once
            # the environment advertises done.
            terminated = True
        self.lives = lives
        return obs, reward, terminated, truncated, info

    def reset(self, **kwargs) -> AtariResetReturn:
        """
        Calls the Gym environment 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.

        :param kwargs: Extra keywords passed to env.reset() call
        :return: the first observation of the environment
        """
        if self.was_real_done:
            obs, info = self.env.reset(**kwargs)
        else:
            # no-op step to advance from terminal/lost life state
            obs, _, terminated, truncated, info = self.env.step(0)

            # The no-op step can lead to a game over, so we need to check it again
            # to see if we should reset the environment and avoid the
            # monitor.py `RuntimeError: Tried to step environment that needs reset`
            if terminated or truncated:
                obs, info = self.env.reset(**kwargs)
        self.lives = self.env.unwrapped.ale.lives()  # type: ignore[attr-defined]
        return obs, info


class MaxAndSkipEnv(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
    """
    Return only every ``skip``-th frame (frameskipping)
    and return the max between the two last frames.

    :param env: Environment to wrap
    :param skip: Number of ``skip``-th frame
        The same action will be taken ``skip`` times.
    """

    def __init__(self, env: gym.Env, skip: int = 4) -> None:
        super().__init__(env)
        # most recent raw observations (for max pooling across time steps)
        assert env.observation_space.dtype is not None, "No dtype specified for the observation space"
        assert env.observation_space.shape is not None, "No shape defined for the observation space"
        self._obs_buffer = np.zeros((2, *env.observation_space.shape), dtype=env.observation_space.dtype)
        self._skip = skip

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

        :param action: the action
        :return: observation, reward, terminated, truncated, information
        """
        total_reward = 0.0
        terminated = truncated = False
        for i in range(self._skip):
            obs, reward, terminated, truncated, info = self.env.step(action)
            done = terminated or truncated
            if i == self._skip - 2:
                self._obs_buffer[0] = obs
            if i == self._skip - 1:
                self._obs_buffer[1] = obs
            total_reward += float(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, terminated, truncated, info


class ClipRewardEnv(gym.RewardWrapper):
    """
    Clip the reward to {+1, 0, -1} by its sign.

    :param env: Environment to wrap
    """

    def __init__(self, env: gym.Env) -> None:
        super().__init__(env)

    def reward(self, reward: SupportsFloat) -> float:
        """
        Bin reward to {+1, 0, -1} by its sign.

        :param reward:
        :return:
        """
        return np.sign(float(reward))


class WarpFrame(gym.ObservationWrapper[np.ndarray, int, np.ndarray]):
    """
    Convert to grayscale and warp frames to 84x84 (default)
    as done in the Nature paper and later work.

    :param env: Environment to wrap
    :param width: New frame width
    :param height: New frame height
    """

    def __init__(self, env: gym.Env, width: int = 84, height: int = 84) -> None:
        super().__init__(env)
        self.width = width
        self.height = height
        assert isinstance(env.observation_space, spaces.Box), f"Expected Box space, got {env.observation_space}"

        self.observation_space = spaces.Box(
            low=0,
            high=255,
            shape=(self.height, self.width, 1),
            dtype=env.observation_space.dtype,  # type: ignore[arg-type]
        )

    def observation(self, frame: np.ndarray) -> np.ndarray:
        """
        returns the current observation from a frame

        :param frame: environment frame
        :return: the observation
        """
        assert cv2 is not None, "OpenCV is not installed, you can do `pip install opencv-python`"
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
        frame = cv2.resize(frame, (self.width, self.height), interpolation=cv2.INTER_AREA)
        return frame[:, :, None]


class AtariWrapper(gym.Wrapper[np.ndarray, int, np.ndarray, int]):
    """
    Atari 2600 preprocessings

    Specifically:

    * Noop reset: obtain initial state by taking random number of no-ops on reset.
    * Frame skipping: 4 by default
    * Max-pooling: most recent two observations
    * Termination signal when a life is lost.
    * Resize to a square image: 84x84 by default
    * Grayscale observation
    * Clip reward to {-1, 0, 1}
    * Sticky actions: disabled by default

    See https://danieltakeshi.github.io/2016/11/25/frame-skipping-and-preprocessing-for-deep-q-networks-on-atari-2600-games/
    for a visual explanation.

    .. warning::
        Use this wrapper only with Atari v4 without frame skip: ``env_id = "*NoFrameskip-v4"``.

    :param env: Environment to wrap
    :param noop_max: Max number of no-ops
    :param frame_skip: Frequency at which the agent experiences the game.
        This correspond to repeating the action ``frame_skip`` times.
    :param screen_size: Resize Atari frame
    :param terminal_on_life_loss: If True, then step() returns done=True whenever a life is lost.
    :param clip_reward: If True (default), the reward is clip to {-1, 0, 1} depending on its sign.
    :param action_repeat_probability: Probability of repeating the last action
    """

    def __init__(
        self,
        env: gym.Env,
        noop_max: int = 30,
        frame_skip: int = 4,
        screen_size: int = 84,
        terminal_on_life_loss: bool = True,
        clip_reward: bool = True,
        action_repeat_probability: float = 0.0,
    ) -> None:
        if action_repeat_probability > 0.0:
            env = StickyActionEnv(env, action_repeat_probability)
        if noop_max > 0:
            env = NoopResetEnv(env, noop_max=noop_max)
        # frame_skip=1 is the same as no frame-skip (action repeat)
        if frame_skip > 1:
            env = MaxAndSkipEnv(env, skip=frame_skip)
        if terminal_on_life_loss:
            env = EpisodicLifeEnv(env)
        if "FIRE" in env.unwrapped.get_action_meanings():  # type: ignore[attr-defined]
            env = FireResetEnv(env)
        env = WarpFrame(env, width=screen_size, height=screen_size)
        if clip_reward:
            env = ClipRewardEnv(env)

        super().__init__(env)