Added is_np_flattenable property to spaces docs (#172)

CONTRIBUTING.md

@@ -46,4 +46,17 @@ In addition, new file and class require top docstrings that should outline the p
 For classes, code block examples can be provided in the top docstring and not the constructor arguments.
 
 To check your docstrings are correct, run `pre-commit run --all-files` or `pydocstyle --source --explain --convention=google`.
-If all docstrings that fail, the source and reason for the failure is provided. 
+If all docstrings that fail, the source and reason for the failure is provided. 
+
+## Building the docs
+Make sure that you have install the requirements:
+```shell
+cd docs
+pip install -r requirements.txt
+```
+Then run
+```
+python scripts/gen_mds.py 
+make dirhtml
+```
+Now, navigate to `_build/dirhtml` and open `index.html` in your browser.

docs/api/spaces.md

@@ -13,20 +13,26 @@ spaces/utils
 spaces/vector_utils
 ```
 
+```{eval-rst}
+.. automodule:: gymnasium.spaces
+```
+
+## The Base Class
 ```{eval-rst}
 .. autoclass:: gymnasium.spaces.Space
 ```
 
-## Attributes
+### Attributes
 
 ```{eval-rst}
 .. autoproperty:: gymnasium.spaces.space.Space.shape
 .. property:: Space.dtype
 
     Return the data type of this space.
+.. autoproperty:: gymnasium.spaces.space.Space.is_np_flattenable
 ```
 
-## Methods
+### Methods
 
 Each space implements the following functions:
 

gymnasium/spaces/__init__.py

@@ -7,6 +7,8 @@
 
     self.action_space = spaces.Discrete(3)
     self.observation_space = spaces.Box(0, 1, shape=(2,))
+
+All spaces inherit from the :class:`Space` superclass.
 """
 from gymnasium.spaces.box import Box
 from gymnasium.spaces.dict import Dict

gymnasium/spaces/space.py

@@ -85,7 +85,7 @@ def shape(self) -> tuple[int, ...] | None:
 
     @property
     def is_np_flattenable(self) -> bool:
-        """Checks whether this space can be flattened to a :class:`spaces.Box`."""
+        """Checks whether this space can be flattened to a :class:`gymnasium.spaces.Box`."""
         raise NotImplementedError
 
     def sample(self, mask: Any | None = None) -> T_cov: