Add deprecated wrapper error in gymnasium.experimental.wrappers (#341)

.github/workflows/build.yml

@@ -18,7 +18,7 @@ jobs:
              --tag gymnasium-all-docker .
       - name: Run tests
         run: docker run gymnasium-all-docker pytest tests/*
-      - name: Run doctest
+      - name: Run doctests
         run: docker run gymnasium-all-docker pytest --doctest-modules gymnasium/
 
   build-necessary:

docs/api/experimental.md

@@ -101,7 +101,7 @@ We aimed to replace the wrappers in gymnasium v0.30.0 with these experimental wr
     * - `supersuit.clip_reward_v0 <https://github.com/Farama-Foundation/SuperSuit/blob/314831a7d18e7254f455b181694c049908f95155/supersuit/generic_wrappers/basic_wrappers.py#L74>`_
       - :class:`experimental.wrappers.ClipRewardV0`
     * - :class:`wrappers.NormalizeReward`
-      - :class:`experimental.wrappers.NormalizeRewardV0`
+      - :class:`experimental.wrappers.NormalizeRewardV1`
 ```
 
 ### Common Wrappers

docs/api/experimental/wrappers.md

@@ -37,7 +37,7 @@ title: Wrappers
 ```{eval-rst}
 .. autoclass:: gymnasium.experimental.wrappers.LambdaRewardV0
 .. autoclass:: gymnasium.experimental.wrappers.ClipRewardV0
-.. autoclass:: gymnasium.experimental.wrappers.NormalizeRewardV0
+.. autoclass:: gymnasium.experimental.wrappers.NormalizeRewardV1
 ```
 
 ## Other Wrappers

gymnasium/error.py

@@ -181,6 +181,10 @@ class RetriesExceededError(Error):
     """Error message for retries exceeding set number."""
 
 
+class DeprecatedWrapper(ImportError):
+    """Error message for importing an old version of a wrapper."""
+
+
 # Vectorized environments errors
 
 

gymnasium/experimental/wrappers/__init__.py

@@ -1,7 +1,9 @@
 """`__init__` for experimental wrappers, to avoid loading the wrappers if unnecessary, we can hack python."""
 # pyright: reportUnsupportedDunderAll=false
-
 import importlib
+import re
+
+from gymnasium.error import DeprecatedWrapper
 
 
 __all__ = [
@@ -30,7 +32,7 @@
     # --- Reward wrappers ---
     "LambdaRewardV0",
     "ClipRewardV0",
-    "NormalizeRewardV0",
+    "NormalizeRewardV1",
     # --- Common ---
     "AutoresetV0",
     "PassiveEnvCheckerV0",
@@ -66,7 +68,7 @@
     # lambda_reward.py
     "ClipRewardV0": "lambda_reward",
     "LambdaRewardV0": "lambda_reward",
-    "NormalizeRewardV0": "lambda_reward",
+    "NormalizeRewardV1": "lambda_reward",
     # stateful_action
     "StickyActionV0": "stateful_action",
     # stateful_observation
@@ -99,21 +101,64 @@
 }
 
 
-def __getattr__(name: str):
-    """To avoid having to load all wrappers on `import gymnasium` with all of their extra modules.
+def __getattr__(wrapper_name: str):
+    """Load a wrapper by name.
 
-    This optimises the loading of gymnasium.
+    This optimizes the loading of gymnasium wrappers by only loading the wrapper if it is used.
+    Errors will be raised if the wrapper does not exist or if the version is not the latest.
 
     Args:
-        name: The name of a wrapper to load
+        wrapper_name: The name of a wrapper to load.
 
     Returns:
-        Wrapper
+        The specified wrapper.
+
+    Raises:
+        AttributeError: If the wrapper does not exist.
+        DeprecatedWrapper: If the version is not the latest.
     """
-    if name in _wrapper_to_class:
-        import_stmt = f"gymnasium.experimental.wrappers.{_wrapper_to_class[name]}"
+    # Check if the requested wrapper is in the _wrapper_to_class dictionary
+    if wrapper_name in _wrapper_to_class:
+        import_stmt = (
+            f"gymnasium.experimental.wrappers.{_wrapper_to_class[wrapper_name]}"
+        )
         module = importlib.import_module(import_stmt)
-        return getattr(module, name)
-    # add helpful error message if version number has changed
+        return getattr(module, wrapper_name)
+
+    # Define a regex pattern to match the integer suffix (version number) of the wrapper
+    int_suffix_pattern = r"(\d+)$"
+    version_match = re.search(int_suffix_pattern, wrapper_name)
+
+    # If a version number is found, extract it and the base wrapper name
+    if version_match:
+        version = int(version_match.group())
+        base_name = wrapper_name[: -len(version_match.group())]
+    else:
+        version = float("inf")
+        base_name = wrapper_name[:-2]
+
+    # Filter the list of all wrappers to include only those with the same base name
+    matching_wrappers = [name for name in __all__ if name.startswith(base_name)]
+
+    # If no matching wrappers are found, raise an AttributeError
+    if not matching_wrappers:
+        raise AttributeError(f"module {__name__!r} has no attribute {wrapper_name!r}")
+
+    # Find the latest version of the matching wrappers
+    latest_wrapper = max(
+        matching_wrappers, key=lambda s: int(re.findall(int_suffix_pattern, s)[0])
+    )
+    latest_version = int(re.findall(int_suffix_pattern, latest_wrapper)[0])
+
+    # If the requested wrapper is an older version, raise a DeprecatedWrapper exception
+    if version < latest_version:
+        raise DeprecatedWrapper(
+            f"{wrapper_name!r} is now deprecated, use {latest_wrapper!r} instead.\n"
+            f"To see the changes made, go to "
+            f"https://gymnasium.farama.org/api/experimental/wrappers/#gymnasium.experimental.wrappers.{latest_wrapper}"
+        )
+    # If the requested version is invalid, raise an AttributeError
     else:
-        raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
+        raise AttributeError(
+            f"module {__name__!r} has no attribute {wrapper_name!r}, did you mean {latest_wrapper!r}"
+        )

gymnasium/experimental/wrappers/lambda_reward.py

@@ -100,7 +100,7 @@ def __init__(
         )
 
 
-class NormalizeRewardV0(
+class NormalizeRewardV1(
     gym.Wrapper[ObsType, ActType, ObsType, ActType], gym.utils.RecordConstructorArgs
 ):
     r"""This wrapper will normalize immediate rewards s.t. their exponential moving average has a fixed variance.
@@ -111,6 +111,10 @@ class NormalizeRewardV0(
     statistics. If `True` (default), the `RunningMeanStd` will get updated every time `self.normalize()` is called.
     If False, the calculated statistics are used but not updated anymore; this may be used during evaluation.
 
+    Note:
+        In v0.27, NormalizeReward was updated as the forward discounted reward estimate was incorrect computed in Gym v0.25+.
+        For more detail, read [#3154](https://github.com/openai/gym/pull/3152).
+
     Note:
         The scaling depends on past trajectories and rewards will not be scaled correctly if the wrapper was newly
         instantiated or the policy was changed recently.

tests/experimental/wrappers/test_import_wrappers.py

@@ -0,0 +1,43 @@
+"""Test suite for import wrappers."""
+
+import re
+
+import pytest
+
+import gymnasium.experimental.wrappers as wrappers
+
+
+def test_import_wrappers():
+    """Test that all wrappers can be imported."""
+    # Test that a deprecated wrapper raises a DeprecatedWrapper
+    with pytest.raises(
+        wrappers.DeprecatedWrapper,
+        match=re.escape("'NormalizeRewardV0' is now deprecated"),
+    ):
+        getattr(wrappers, "NormalizeRewardV0")
+
+    # Test that an invalid version raises an AttributeError
+    with pytest.raises(
+        AttributeError,
+        match=re.escape(
+            "module 'gymnasium.experimental.wrappers' has no attribute 'ClipRewardVT', did you mean"
+        ),
+    ):
+        getattr(wrappers, "ClipRewardVT")
+
+    with pytest.raises(
+        AttributeError,
+        match=re.escape(
+            "module 'gymnasium.experimental.wrappers' has no attribute 'ClipRewardV99', did you mean"
+        ),
+    ):
+        getattr(wrappers, "ClipRewardV99")
+
+    # Test that an invalid wrapper raises an AttributeError
+    with pytest.raises(
+        AttributeError,
+        match=re.escape(
+            "module 'gymnasium.experimental.wrappers' has no attribute 'NonexistentWrapper'"
+        ),
+    ):
+        getattr(wrappers, "NonexistentWrapper")

tests/experimental/wrappers/test_normalize_reward.py

@@ -1,8 +1,8 @@
-"""Test suite for NormalizeRewardV0."""
+"""Test suite for NormalizeRewardV1."""
 import numpy as np
 
 from gymnasium.core import ActType
-from gymnasium.experimental.wrappers import NormalizeRewardV0
+from gymnasium.experimental.wrappers import NormalizeRewardV1
 from tests.testing_env import GenericTestEnv
 
 
@@ -18,7 +18,7 @@ def step_func(self, action: ActType):
 def test_running_mean_normalize_reward_wrapper():
     """Tests that the property `_update_running_mean` freezes/continues the running statistics updating."""
     env = _make_reward_env()
-    wrapped_env = NormalizeRewardV0(env)
+    wrapped_env = NormalizeRewardV1(env)
 
     # Default value is True
     assert wrapped_env.update_running_mean
@@ -48,7 +48,7 @@ def test_normalize_reward_wrapper():
     """Tests that the NormalizeReward does not throw an error."""
     # TODO: Functional correctness should be tested
     env = _make_reward_env()
-    wrapped_env = NormalizeRewardV0(env)
+    wrapped_env = NormalizeRewardV1(env)
     wrapped_env.reset()
     _, reward, _, _, _ = wrapped_env.step(None)
     assert np.ndim(reward) == 0