deprecate built in wrappers for supersuit

gym/wrappers/README.md

@@ -1,3 +1,9 @@
+# Deprecation
+
+While Gym's wrappers will continue to work for the foreseeable future due to the widespread dependence on them throughout the community, we are deprecating them and encourage users to use [SuperSuit](https://github.com/PettingZoo-Team/SuperSuit) instead.
+
+# Old Docs:
+
 # Wrappers
 
 Wrappers are used to transform an environment in a modular way:

gym/wrappers/atari_preprocessing.py

@@ -1,5 +1,5 @@
 import numpy as np
-
+import warnings
 import gym
 from gym.spaces import Box
 from gym.wrappers import TimeLimit
@@ -67,6 +67,7 @@ class AtariPreprocessing(gym.Wrapper):
             )
         self.noop_max = noop_max
         assert env.unwrapped.get_action_meanings()[0] == "NOOP"
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
 
         self.frame_skip = frame_skip
         self.screen_size = screen_size

gym/wrappers/clip_action.py

@@ -1,5 +1,5 @@
 import numpy as np
-
+import warnings
 from gym import ActionWrapper
 from gym.spaces import Box
 
@@ -9,6 +9,7 @@ class ClipAction(ActionWrapper):
 
     def __init__(self, env):
         assert isinstance(env.action_space, Box)
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
         super(ClipAction, self).__init__(env)
 
     def action(self, action):

gym/wrappers/filter_observation.py

@@ -1,5 +1,5 @@
 import copy
-
+import warnings
 from gym import spaces
 from gym import ObservationWrapper
 
@@ -26,6 +26,7 @@ class FilterObservation(ObservationWrapper):
         assert isinstance(
             wrapped_observation_space, spaces.Dict
         ), "FilterObservationWrapper is only usable with dict observations."
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
 
         observation_keys = wrapped_observation_space.spaces.keys()
 

gym/wrappers/flatten_observation.py

@@ -1,5 +1,6 @@
 import gym.spaces as spaces
 from gym import ObservationWrapper
+import warnings
 
 
 class FlattenObservation(ObservationWrapper):
@@ -8,6 +9,7 @@ class FlattenObservation(ObservationWrapper):
     def __init__(self, env):
         super(FlattenObservation, self).__init__(env)
         self.observation_space = spaces.flatten_space(env.observation_space)
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
 
     def observation(self, observation):
         return spaces.flatten(self.env.observation_space, observation)

gym/wrappers/frame_stack.py

@@ -1,6 +1,6 @@
 from collections import deque
 import numpy as np
-
+import warnings
 from gym.spaces import Box
 from gym import Wrapper
 
@@ -22,6 +22,7 @@ class LazyFrames(object):
     __slots__ = ("frame_shape", "dtype", "shape", "lz4_compress", "_frames")
 
     def __init__(self, frames, lz4_compress=False):
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
         self.frame_shape = tuple(frames[0].shape)
         self.shape = (len(frames),) + self.frame_shape
         self.dtype = frames[0].dtype

gym/wrappers/gray_scale_observation.py

@@ -1,5 +1,5 @@
 import numpy as np
-
+import warnings
 from gym.spaces import Box
 from gym import ObservationWrapper
 
@@ -15,6 +15,7 @@ class GrayScaleObservation(ObservationWrapper):
             len(env.observation_space.shape) == 3
             and env.observation_space.shape[-1] == 3
         )
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
         obs_shape = self.observation_space.shape[:2]
         if self.keep_dim:
             self.observation_space = Box(

gym/wrappers/pixel_observation.py

@@ -1,9 +1,7 @@
-"""An observation wrapper that augments observations by pixel values."""
-
 import collections
 from collections.abc import MutableMapping
 import copy
-
+import warnings
 import numpy as np
 
 from gym import spaces
@@ -54,6 +52,8 @@ class PixelObservationWrapper(ObservationWrapper):
             assert render_mode == "rgb_array", render_mode
             render_kwargs[key]["mode"] = "rgb_array"
 
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
+
         wrapped_observation_space = env.observation_space
 
         if isinstance(wrapped_observation_space, spaces.Box):

gym/wrappers/record_episode_statistics.py

@@ -1,6 +1,6 @@
 import time
 from collections import deque
-
+import warnings
 import gym
 
 
@@ -14,6 +14,7 @@ class RecordEpisodeStatistics(gym.Wrapper):
         self.episode_length = 0
         self.return_queue = deque(maxlen=deque_size)
         self.length_queue = deque(maxlen=deque_size)
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
 
     def reset(self, **kwargs):
         observation = super(RecordEpisodeStatistics, self).reset(**kwargs)

gym/wrappers/rescale_action.py

@@ -1,5 +1,5 @@
 import numpy as np
-
+import warnings
 import gym
 from gym import spaces
 
@@ -19,6 +19,7 @@ class RescaleAction(gym.ActionWrapper):
             env.action_space, spaces.Box
         ), "expected Box action space, got {}".format(type(env.action_space))
         assert np.less_equal(a, b).all(), (a, b)
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
         super(RescaleAction, self).__init__(env)
         self.a = np.zeros(env.action_space.shape, dtype=env.action_space.dtype) + a
         self.b = np.zeros(env.action_space.shape, dtype=env.action_space.dtype) + b

gym/wrappers/resize_observation.py

@@ -1,5 +1,5 @@
 import numpy as np
-
+import warnings
 from gym.spaces import Box
 from gym import ObservationWrapper
 
@@ -12,6 +12,7 @@ class ResizeObservation(ObservationWrapper):
         if isinstance(shape, int):
             shape = (shape, shape)
         assert all(x > 0 for x in shape), shape
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
         self.shape = tuple(shape)
 
         obs_shape = self.shape + self.observation_space.shape[2:]

gym/wrappers/time_aware_observation.py

@@ -1,5 +1,5 @@
 import numpy as np
-
+import warnings
 from gym.spaces import Box
 from gym import ObservationWrapper
 
@@ -17,6 +17,7 @@ class TimeAwareObservation(ObservationWrapper):
         super(TimeAwareObservation, self).__init__(env)
         assert isinstance(env.observation_space, Box)
         assert env.observation_space.dtype == np.float32
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
         low = np.append(self.observation_space.low, 0.0)
         high = np.append(self.observation_space.high, np.inf)
         self.observation_space = Box(low, high, dtype=np.float32)

gym/wrappers/time_limit.py

@@ -1,9 +1,11 @@
 import gym
+import warnings
 
 
 class TimeLimit(gym.Wrapper):
     def __init__(self, env, max_episode_steps=None):
         super(TimeLimit, self).__init__(env)
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
         if max_episode_steps is None and self.env.spec is not None:
             max_episode_steps = env.spec.max_episode_steps
         if self.env.spec is not None:

gym/wrappers/transform_observation.py

@@ -1,4 +1,5 @@
 from gym import ObservationWrapper
+import warnings
 
 
 class TransformObservation(ObservationWrapper):
@@ -21,6 +22,7 @@ class TransformObservation(ObservationWrapper):
     def __init__(self, env, f):
         super(TransformObservation, self).__init__(env)
         assert callable(f)
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
         self.f = f
 
     def observation(self, observation):

gym/wrappers/transform_reward.py

@@ -1,4 +1,5 @@
 from gym import RewardWrapper
+import warnings
 
 
 class TransformReward(RewardWrapper):
@@ -23,6 +24,7 @@ class TransformReward(RewardWrapper):
     def __init__(self, env, f):
         super(TransformReward, self).__init__(env)
         assert callable(f)
+        warnings.warn("Gym\'s internal preprocessing wrappers are now deprecated. While they will continue to work for the foreseeable future, we strongly recommend using SuperSuit instead: https://github.com/PettingZoo-Team/SuperSuit")
         self.f = f
 
     def reward(self, reward):