h%SrSSKJr SSKJrJrJr SSKJrJ r J r J r SSK r SSKJr SSKJr \ "SSS 9r\R&\ R(rS \S '"S S \ \5rg)z(Implementation of the `Space` metaclass.) annotations)IterableMappingSequence)AnyGeneric TypeAliasTypeVarN)seedingT_covT) covariantr MaskNDArrayc\rSrSrSrSSSjjr\SSj5r\SSj5r\SSj5r SSSjjr SSS jjr SS jr SS jr SS jrSS jrSSjrSrg)SpaceaSuperclass that is used to define observation and action spaces. Spaces are crucially used in Gym to define the format of valid actions and observations. They serve various purposes: * They clearly define how to interact with environments, i.e. they specify what actions need to look like and what observations will look like * They allow us to work with highly structured data (e.g. in the form of elements of :class:`Dict` spaces) and painlessly transform them into flat arrays that can be used in learning code * They provide a method to sample random elements. This is especially useful for exploration and debugging. Different spaces can be combined hierarchically via container spaces (:class:`Tuple` and :class:`Dict`) to build a more expressive space Warning: Custom observation & action spaces can inherit from the ``Space`` class. However, most use-cases should be covered by the existing space classes (e.g. :class:`Box`, :class:`Discrete`, etc...), and container classes (:class:`Tuple` & :class:`Dict`). Note that parametrized probability distributions (through the :meth:`Space.sample()` method), and batching functions (in :class:`gym.vector.VectorEnv`), are only well-defined for instances of spaces provided in gym by default. Moreover, some implementations of Reinforcement Learning algorithms might not handle custom spaces properly. Use custom spaces with care. NcUcSO [U5UlUcSO[R"U5UlSUlUbB[ U[R R5(aX0lgURU5 gg)akConstructor of :class:`Space`. Args: shape (Optional[Sequence[int]]): If elements of the space are numpy arrays, this should specify their shape. dtype (Optional[Type | str]): If elements of the space are numpy arrays, this should specify their dtype. seed: Optionally, you can use this argument to seed the RNG that is used to sample from the space N) tuple_shapenpdtype _np_random isinstancerandom Generatorseed)selfshaperrs P/home/james-whalen/.local/lib/python3.13/site-packages/gymnasium/spaces/space.py__init__Space.__init__.sc$mdu "]T   $ 3 344"& $ cURcUR5 URc[R"5uUlnUR$)zLazily seed the PRNG since this is expensive and only needed if sampling from this space. As :meth:`seed` is not guaranteed to set the `_np_random` for particular seeds. We add a check after :meth:`seed` to set a new random number generator. )rrr np_random)r_s rr#Space.np_randomDsB ?? " IIK ?? "!(!2!2!4 DOQr!cUR$)z7Return the shape of the space as an immutable property.)rrs rr Space.shapeTs{{r!c[e)zNChecks whether this space can be flattened to a :class:`gymnasium.spaces.Box`.NotImplementedErrorr's ris_np_flattenableSpace.is_np_flattenableYs "!r!c[e)a Randomly sample an element of this space. Can be uniform or non-uniform sampling based on boundedness of space. The binary mask and the probability mask can't be used at the same time. Args: mask: A mask used for random sampling, expected ``dtype=np.int8`` and see sample implementation for expected shape. probability: A probability mask used for sampling according to the given probability distribution, expected ``dtype=np.float64`` and see sample implementation for expected shape. Returns: A sampled actions from the space r*)rmask probabilitys rsample Space.sample^s "!r!cB[R"U5uUlnU$)aSeed the pseudorandom number generator (PRNG) of this space and, if applicable, the PRNGs of subspaces. Args: seed: The seed value for the space. This is expanded for composite spaces to accept multiple values. For further details, please refer to the space's documentation. Returns: The seed values used for all the PRNGs, for composite spaces this can be a tuple or dictionary of values. )r r#r)rrnp_random_seeds rr Space.seedns!+2*;*;D*A'r!c[e)zbReturn boolean specifying if x is a valid member of this space, equivalent to ``sample in space``.r*rxs rcontainsSpace.containszs!!r!c$URU5$)z?Return boolean specifying if x is a valid member of this space.)r9r7s r __contains__Space.__contains__~s}}Qr!c[U5nSU;aURS5US'US SU;a USUS'US URRU5 g)zUsed when loading a pickled space. This method was implemented explicitly to allow for loading of legacy states. Args: state: The updated state value rrr#rN)dictget__dict__update)rstates r __setstate__Space.__setstate__scU  e #ii0E(Og % "' "4E, k" U#r!c[U5$)zCConvert a batch of samples from this space to a JSONable data type.)listrsample_ns r to_jsonableSpace.to_jsonablesH~r!cU$)zCConvert a JSONable data type to a batch of samples from this space.rHs r from_jsonableSpace.from_jsonables r!)rrr)NNN)rzSequence[int] | Nonerznpt.DTypeLike | Nonerz int | np.random.Generator | None)returnznp.random.Generator)rPztuple[int, ...] | None)rPbool)NN)r/ Any | Noner0rRrPr )N)rz int | NonerPz int | list[int] | dict[str, int])r8rrPrQ)rCz-Iterable[tuple[str, Any]] | Mapping[str, Any])rIzSequence[T_cov]rP list[Any])rIrSrPz list[T_cov])__name__ __module__ __qualname____firstlineno____doc__rpropertyr#rr,r1rr9r<rDrJrN__static_attributes__rMr!rrrs6'+&*15  # $ /  ,  """ " $4 r!r)rX __future__rcollections.abcrrrtypingrrr r numpyr numpy.typingnptgymnasium.utilsr r NDArrayint8r__annotations__rrMr!rresY."7733# 4(RWW- Y-PGENPr!