"""Implementation of Atari 2600 Preprocessing following the guidelines of Machado et al., 2018."""
import numpy as np
import gymnasium as gym
from gymnasium.spaces import Box
__all__ = ["AtariPreprocessingV0"]
[docs]
class AtariPreprocessingV0(gym.Wrapper, gym.utils.RecordConstructorArgs):
"""Atari 2600 preprocessing wrapper.
This class follows the guidelines in Machado et al. (2018),
"Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents".
Specifically, the following preprocess stages applies to the atari environment:
- Noop Reset: Obtains the initial state by taking a random number of no-ops on reset, default max 30 no-ops.
- Frame skipping: The number of frames skipped between steps, 4 by default
- Max-pooling: Pools over the most recent two observations from the frame skips
- Termination signal when a life is lost: When the agent losses a life during the environment, then the environment is terminated.
Turned off by default. Not recommended by Machado et al. (2018).
- Resize to a square image: Resizes the atari environment original observation shape from 210x180 to 84x84 by default
- Grayscale observation: If the observation is colour or greyscale, by default, greyscale.
- Scale observation: If to scale the observation between [0, 1) or [0, 255), by default, not scaled.
"""
def __init__(
self,
env: gym.Env,
noop_max: int = 30,
frame_skip: int = 4,
screen_size: int = 84,
terminal_on_life_loss: bool = False,
grayscale_obs: bool = True,
grayscale_newaxis: bool = False,
scale_obs: bool = False,
):
"""Wrapper for Atari 2600 preprocessing.
Args:
env (Env): The environment to apply the preprocessing
noop_max (int): For No-op reset, the max number no-ops actions are taken at reset, to turn off, set to 0.
frame_skip (int): The number of frames between new observation the agents observations effecting the frequency at which the agent experiences the game.
screen_size (int): resize Atari frame
terminal_on_life_loss (bool): `if True`, then :meth:`step()` returns `terminated=True` whenever a
life is lost.
grayscale_obs (bool): if True, then gray scale observation is returned, otherwise, RGB observation
is returned.
grayscale_newaxis (bool): `if True and grayscale_obs=True`, then a channel axis is added to
grayscale observations to make them 3-dimensional.
scale_obs (bool): if True, then observation normalized in range [0,1) is returned. It also limits memory
optimization benefits of FrameStack Wrapper.
Raises:
DependencyNotInstalled: opencv-python package not installed
ValueError: Disable frame-skipping in the original env
"""
gym.utils.RecordConstructorArgs.__init__(
self,
noop_max=noop_max,
frame_skip=frame_skip,
screen_size=screen_size,
terminal_on_life_loss=terminal_on_life_loss,
grayscale_obs=grayscale_obs,
grayscale_newaxis=grayscale_newaxis,
scale_obs=scale_obs,
)
gym.Wrapper.__init__(self, env)
try:
import cv2 # noqa: F401
except ImportError:
raise gym.error.DependencyNotInstalled(
"opencv-python package not installed, run `pip install gymnasium[other]` to get dependencies for atari"
)
assert frame_skip > 0
assert screen_size > 0
assert noop_max >= 0
if frame_skip > 1:
if (
env.spec is not None
and "NoFrameskip" not in env.spec.id
and getattr(env.unwrapped, "_frameskip", None) != 1
):
raise ValueError(
"Disable frame-skipping in the original env. Otherwise, more than one "
"frame-skip will happen as through this wrapper"
)
self.noop_max = noop_max
assert env.unwrapped.get_action_meanings()[0] == "NOOP"
self.frame_skip = frame_skip
self.screen_size = screen_size
self.terminal_on_life_loss = terminal_on_life_loss
self.grayscale_obs = grayscale_obs
self.grayscale_newaxis = grayscale_newaxis
self.scale_obs = scale_obs
# buffer of most recent two observations for max pooling
assert isinstance(env.observation_space, Box)
if grayscale_obs:
self.obs_buffer = [
np.empty(env.observation_space.shape[:2], dtype=np.uint8),
np.empty(env.observation_space.shape[:2], dtype=np.uint8),
]
else:
self.obs_buffer = [
np.empty(env.observation_space.shape, dtype=np.uint8),
np.empty(env.observation_space.shape, dtype=np.uint8),
]
self.lives = 0
self.game_over = False
_low, _high, _obs_dtype = (
(0, 255, np.uint8) if not scale_obs else (0, 1, np.float32)
)
_shape = (screen_size, screen_size, 1 if grayscale_obs else 3)
if grayscale_obs and not grayscale_newaxis:
_shape = _shape[:-1] # Remove channel axis
self.observation_space = Box(
low=_low, high=_high, shape=_shape, dtype=_obs_dtype
)
@property
def ale(self):
"""Make ale as a class property to avoid serialization error."""
return self.env.unwrapped.ale
def step(self, action):
"""Applies the preprocessing for an :meth:`env.step`."""
total_reward, terminated, truncated, info = 0.0, False, False, {}
for t in range(self.frame_skip):
_, reward, terminated, truncated, info = self.env.step(action)
total_reward += reward
self.game_over = terminated
if self.terminal_on_life_loss:
new_lives = self.ale.lives()
terminated = terminated or new_lives < self.lives
self.game_over = terminated
self.lives = new_lives
if terminated or truncated:
break
if t == self.frame_skip - 2:
if self.grayscale_obs:
self.ale.getScreenGrayscale(self.obs_buffer[1])
else:
self.ale.getScreenRGB(self.obs_buffer[1])
elif t == self.frame_skip - 1:
if self.grayscale_obs:
self.ale.getScreenGrayscale(self.obs_buffer[0])
else:
self.ale.getScreenRGB(self.obs_buffer[0])
return self._get_obs(), total_reward, terminated, truncated, info
def reset(self, **kwargs):
"""Resets the environment using preprocessing."""
# NoopReset
_, reset_info = self.env.reset(**kwargs)
noops = (
self.env.unwrapped.np_random.integers(1, self.noop_max + 1)
if self.noop_max > 0
else 0
)
for _ in range(noops):
_, _, terminated, truncated, step_info = self.env.step(0)
reset_info.update(step_info)
if terminated or truncated:
_, reset_info = self.env.reset(**kwargs)
self.lives = self.ale.lives()
if self.grayscale_obs:
self.ale.getScreenGrayscale(self.obs_buffer[0])
else:
self.ale.getScreenRGB(self.obs_buffer[0])
self.obs_buffer[1].fill(0)
return self._get_obs(), reset_info
def _get_obs(self):
if self.frame_skip > 1: # more efficient in-place pooling
np.maximum(self.obs_buffer[0], self.obs_buffer[1], out=self.obs_buffer[0])
import cv2
obs = cv2.resize(
self.obs_buffer[0],
(self.screen_size, self.screen_size),
interpolation=cv2.INTER_AREA,
)
if self.scale_obs:
obs = np.asarray(obs, dtype=np.float32) / 255.0
else:
obs = np.asarray(obs, dtype=np.uint8)
if self.grayscale_obs and self.grayscale_newaxis:
obs = np.expand_dims(obs, axis=-1) # Add a channel axis
return obs
