Remove outdated 3.8 and 3.9 references

Add ToArray wrapper to docs
Add array-api-extra for reliable testing
Fix smaller mistakes in the docs
Add NumPy version check for Array API wrappers

Author: amacati

Diff for: README.md

@@ -31,7 +31,7 @@ To install the base Gymnasium library, use `pip install gymnasium`
 
 This does not include dependencies for all families of environments (there's a massive number, and some can be problematic to install on certain systems). You can install these dependencies for one family like `pip install "gymnasium[atari]"` or use `pip install "gymnasium[all]"` to install all dependencies.
 
-We support and test for Python 3.8, 3.9, 3.10, 3.11 and 3.12 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.
+We support and test for Python 3.10, 3.11 and 3.12 on Linux and macOS. We will accept PRs related to Windows, but do not officially support it.
 
 ## API
 

Diff for: docs/api/wrappers/misc_wrappers.md

@@ -27,6 +27,7 @@ title: Misc Wrappers
 ## Data Conversion Wrappers
 
 ```{eval-rst}
+.. autoclass:: gymnasium.wrappers.ToArray
 .. autoclass:: gymnasium.wrappers.JaxToNumpy
 .. autoclass:: gymnasium.wrappers.JaxToTorch
 .. autoclass:: gymnasium.wrappers.NumpyToTorch

Diff for: docs/api/wrappers/table.md

@@ -34,6 +34,8 @@ wrapper in the page on the wrapper type
       - Converts an image observation computed by ``reset`` and ``step`` from RGB to Grayscale.
     * - :class:`HumanRendering`
       - Allows human like rendering for environments that support "rgb_array" rendering.
+    * - :class:`ToArray`
+      - Wraps an environment based on any Array API compatible framework, e.g. torch, jax, numpy, such that it can be interacted with any other Array API compatible framework.
     * - :class:`JaxToNumpy`
       - Wraps a Jax-based environment such that it can be interacted with NumPy arrays.
     * - :class:`JaxToTorch`

Diff for: gymnasium/wrappers/to_array.py

@@ -19,9 +19,12 @@
 import functools
 import numbers
 from collections import abc
-from types import ModuleType
+from types import ModuleType, NoneType
 from typing import Any, Iterable, Mapping, SupportsFloat
 
+import numpy as np
+from packaging.version import Version
+
 import gymnasium as gym
 from gymnasium.core import RenderFrame, WrapperActType, WrapperObsType
 from gymnasium.error import DependencyNotInstalled
@@ -36,10 +39,12 @@
     )
 
 
+if Version(np.__version__) < Version("2.1.0"):
+    raise DependencyNotInstalled("Array API functionality requires numpy >= 2.1.0")
+
+
 __all__ = ["ToArray", "to_xp"]
 
-# The NoneType is not defined in Python 3.9. Remove when the minimal version is bumped to >=3.10
-_NoneType = type(None)
 Array = Any  # TODO: Switch to ArrayAPI type once https://github.com/data-apis/array-api/pull/589 is merged
 Device = Any  # TODO: Switch to ArrayAPI type if available
 
@@ -85,7 +90,7 @@ def module_namespace(module: ModuleType) -> ModuleType:
 
 @functools.singledispatch
 def to_xp(value: Any, xp: ModuleType, device: Device | None = None) -> Any:
-    """Converts a value into the specified xp module array type."""
+    """Convert a value into the specified xp module array type."""
     raise Exception(
         f"No known conversion for ({type(value)}) to xp module ({xp}) registered. Report as issue on github."
     )
@@ -103,40 +108,47 @@ def _number_to_xp(
 def _mapping_to_xp(
     value: Mapping[str, Any], xp: ModuleType, device: Device | None = None
 ) -> Mapping[str, Any]:
-    """Converts a mapping of PyTorch Tensors into a Dictionary of Jax Array."""
+    """Convert a mapping of Arrays into a Dictionary of the specified xp module array type."""
     return type(value)(**{k: to_xp(v, xp, device) for k, v in value.items()})
 
 
 @to_xp.register(abc.Iterable)
 def _iterable_to_xp(
     value: Iterable[Any], xp: ModuleType, device: Device | None = None
 ) -> Iterable[Any]:
-    """Converts an Iterable from PyTorch Tensors to an iterable of Jax Array."""
+    """Convert an Iterable from Arrays to an iterable of the specified xp module array type."""
     # There is currently no type for ArrayAPI compatible objects, so they fall through to this
     # function registered for any Iterable. If they are arrays, we can convert them directly.
     # We currently cannot pass the device to the from_dlpack function, since it is not supported
     # for some frameworks (see e.g. https://github.com/data-apis/array-api-compat/issues/204)
     if is_array_api_obj(value):
-        try:
-            x = xp.from_dlpack(value)
-            return to_device(x, device) if device is not None else x
-        except (RuntimeError, BufferError):
-            # If dlpack fails (e.g. because the array is read-only for frameworks that do not
-            # support it), we create a copy of the array that we own and then convert it.
-            # TODO: The correct treatment of read-only arrays is currently not fully clear in the
-            # Array API. Once ongoing discussions are resolved, we should update this code to remove
-            # any fallbacks.
-            value_namespace = array_namespace(value)
-            value_copy = value_namespace.asarray(value, copy=True)
-            return xp.asarray(value_copy, device=device)
+        return _array_api_to_xp(value, xp, device)
     if hasattr(value, "_make"):
         # namedtuple - underline used to prevent potential name conflicts
         # noinspection PyProtectedMember
         return type(value)._make(to_xp(v, xp, device) for v in value)
     return type(value)(to_xp(v, xp, device) for v in value)
 
 
-@to_xp.register(_NoneType)
+def _array_api_to_xp(
+    value: Array, xp: ModuleType, device: Device | None = None
+) -> Array:
+    """Convert an Array API compatible array to the specified xp module array type."""
+    try:
+        x = xp.from_dlpack(value)
+        return to_device(x, device) if device is not None else x
+    except (RuntimeError, BufferError):
+        # If dlpack fails (e.g. because the array is read-only for frameworks that do not
+        # support it), we create a copy of the array that we own and then convert it.
+        # TODO: The correct treatment of read-only arrays is currently not fully clear in the
+        # Array API. Once ongoing discussions are resolved, we should update this code to remove
+        # any fallbacks.
+        value_namespace = array_namespace(value)
+        value_copy = value_namespace.asarray(value, copy=True)
+        return xp.asarray(value_copy, device=device)
+
+
+@to_xp.register(NoneType)
 def _none_to_xp(value: None, xp: ModuleType, device: Device | None = None) -> None:
     """Passes through None values."""
     return value

Diff for: pyproject.toml

@@ -25,7 +25,6 @@ classifiers = [
 dependencies = [
     "numpy >=1.21.0",
     "cloudpickle >=1.2.0",
-    "importlib-metadata >=4.8.0; python_version < '3.10'",
     "typing-extensions >=4.3.0",
     "farama-notifications >=0.0.1",
 ]
@@ -88,6 +87,7 @@ testing = [
     "pytest >=7.1.3",
     "scipy >=1.7.3",
     "dill >=0.3.7",
+    "array_api_extra >=0.7.0",
 ]
 
 [project.urls]
@@ -131,7 +131,7 @@ exclude = ["tests/**", "**/node_modules", "**/__pycache__"]
 strict = []
 
 typeCheckingMode = "basic"
-pythonVersion = "3.8"
+pythonVersion = "3.10"
 pythonPlatform = "All"
 typeshedPath = "typeshed"
 enableTypeIgnoreComments = true

Diff for: tests/wrappers/test_to_array.py

@@ -10,6 +10,7 @@
 
 
 array_api_compat = pytest.importorskip("array_api_compat")
+array_api_extra = pytest.importorskip("array_api_extra")
 
 from array_api_compat import array_namespace, is_array_api_obj  # noqa: E402
 
@@ -51,12 +52,7 @@ def xp_data_equivalence(data_1, data_2) -> bool:
                 xp_data_equivalence(o_1, o_2) for o_1, o_2 in zip(data_1, data_2)
             )
         elif is_array_api_obj(data_1):
-            # Avoid a dependency on array-api-extra
-            # Otherwise, we could use xpx.isclose(data_1, data_2, atol=0.00001).all()
-            same_device = data_1.device == data_2.device
-            a = np.asarray(data_1)
-            b = np.asarray(data_2)
-            return np.allclose(a, b, atol=0.00001) and same_device
+            return array_api_extra.isclose(data_1, data_2, atol=0.00001).all()
         else:
             return data_1 == data_2
     else: