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<h1>Source code for gymnasium.spaces.space</h1><div class="highlight"><pre>
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<span></span><span class="sd">"""Implementation of the `Space` metaclass."""</span>
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<span class="kn">from</span><span class="w"> </span><span class="nn">__future__</span><span class="w"> </span><span class="kn">import</span> <span class="n">annotations</span>
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<span class="kn">from</span><span class="w"> </span><span class="nn">collections.abc</span><span class="w"> </span><span class="kn">import</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">Mapping</span><span class="p">,</span> <span class="n">Sequence</span>
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<span class="kn">from</span><span class="w"> </span><span class="nn">typing</span><span class="w"> </span><span class="kn">import</span> <span class="n">Any</span><span class="p">,</span> <span class="n">Generic</span><span class="p">,</span> <span class="n">TypeAlias</span><span class="p">,</span> <span class="n">TypeVar</span>
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<span class="kn">import</span><span class="w"> </span><span class="nn">numpy</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">np</span>
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<span class="kn">import</span><span class="w"> </span><span class="nn">numpy.typing</span><span class="w"> </span><span class="k">as</span><span class="w"> </span><span class="nn">npt</span>
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<span class="kn">from</span><span class="w"> </span><span class="nn">gymnasium.utils</span><span class="w"> </span><span class="kn">import</span> <span class="n">seeding</span>
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<span class="n">T_cov</span> <span class="o">=</span> <span class="n">TypeVar</span><span class="p">(</span><span class="s2">"T_cov"</span><span class="p">,</span> <span class="n">covariant</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<span class="n">MaskNDArray</span><span class="p">:</span> <span class="n">TypeAlias</span> <span class="o">=</span> <span class="n">npt</span><span class="o">.</span><span class="n">NDArray</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">int8</span><span class="p">]</span>
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<div class="viewcode-block" id="Space">
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<a class="viewcode-back" href="../../../../api/spaces/#gymnasium.spaces.Space">[docs]</a>
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<span class="k">class</span><span class="w"> </span><span class="nc">Space</span><span class="p">(</span><span class="n">Generic</span><span class="p">[</span><span class="n">T_cov</span><span class="p">]):</span>
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<span class="w"> </span><span class="sd">"""Superclass that is used to define observation and action spaces.</span>
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<span class="sd"> Spaces are crucially used in Gym to define the format of valid actions and observations.</span>
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<span class="sd"> They serve various purposes:</span>
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<span class="sd"> * They clearly define how to interact with environments, i.e. they specify what actions need to look like</span>
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<span class="sd"> and what observations will look like</span>
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<span class="sd"> * They allow us to work with highly structured data (e.g. in the form of elements of :class:`Dict` spaces)</span>
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<span class="sd"> and painlessly transform them into flat arrays that can be used in learning code</span>
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<span class="sd"> * They provide a method to sample random elements. This is especially useful for exploration and debugging.</span>
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<span class="sd"> Different spaces can be combined hierarchically via container spaces (:class:`Tuple` and :class:`Dict`) to build a</span>
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<span class="sd"> more expressive space</span>
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<span class="sd"> Warning:</span>
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<span class="sd"> Custom observation & action spaces can inherit from the ``Space``</span>
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<span class="sd"> class. However, most use-cases should be covered by the existing space</span>
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<span class="sd"> classes (e.g. :class:`Box`, :class:`Discrete`, etc...), and container classes (:class:`Tuple` &</span>
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<span class="sd"> :class:`Dict`). Note that parametrized probability distributions (through the</span>
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<span class="sd"> :meth:`Space.sample()` method), and batching functions (in :class:`gym.vector.VectorEnv`), are</span>
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<span class="sd"> only well-defined for instances of spaces provided in gym by default.</span>
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<span class="sd"> Moreover, some implementations of Reinforcement Learning algorithms might</span>
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<span class="sd"> not handle custom spaces properly. Use custom spaces with care.</span>
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<span class="sd"> """</span>
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<span class="k">def</span><span class="w"> </span><span class="fm">__init__</span><span class="p">(</span>
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<span class="bp">self</span><span class="p">,</span>
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<span class="n">shape</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
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<span class="n">dtype</span><span class="p">:</span> <span class="n">npt</span><span class="o">.</span><span class="n">DTypeLike</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
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<span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">Generator</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
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<span class="p">):</span>
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<span class="w"> </span><span class="sd">"""Constructor of :class:`Space`.</span>
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<span class="sd"> Args:</span>
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<span class="sd"> shape (Optional[Sequence[int]]): If elements of the space are numpy arrays, this should specify their shape.</span>
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<span class="sd"> dtype (Optional[Type | str]): If elements of the space are numpy arrays, this should specify their dtype.</span>
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<span class="sd"> seed: Optionally, you can use this argument to seed the RNG that is used to sample from the space</span>
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<span class="sd"> """</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_shape</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">shape</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">shape</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">dtype</span> <span class="o">=</span> <span class="kc">None</span> <span class="k">if</span> <span class="n">dtype</span> <span class="ow">is</span> <span class="kc">None</span> <span class="k">else</span> <span class="n">np</span><span class="o">.</span><span class="n">dtype</span><span class="p">(</span><span class="n">dtype</span><span class="p">)</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_np_random</span> <span class="o">=</span> <span class="kc">None</span>
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<span class="k">if</span> <span class="n">seed</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
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<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">seed</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">Generator</span><span class="p">):</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_np_random</span> <span class="o">=</span> <span class="n">seed</span>
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<span class="k">else</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
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<span class="nd">@property</span>
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<span class="k">def</span><span class="w"> </span><span class="nf">np_random</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">Generator</span><span class="p">:</span>
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<span class="w"> </span><span class="sd">"""Lazily seed the PRNG since this is expensive and only needed if sampling from this space.</span>
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<span class="sd"> As :meth:`seed` is not guaranteed to set the `_np_random` for particular seeds. We add a</span>
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<span class="sd"> check after :meth:`seed` to set a new random number generator.</span>
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<span class="sd"> """</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_np_random</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">seed</span><span class="p">()</span>
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<span class="c1"># As `seed` is not guaranteed (in particular for composite spaces) to set the `_np_random` then we set it randomly.</span>
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<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_np_random</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
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<span class="bp">self</span><span class="o">.</span><span class="n">_np_random</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">seeding</span><span class="o">.</span><span class="n">np_random</span><span class="p">()</span>
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_np_random</span>
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<span class="nd">@property</span>
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<span class="k">def</span><span class="w"> </span><span class="nf">shape</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">|</span> <span class="kc">None</span><span class="p">:</span>
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<span class="w"> </span><span class="sd">"""Return the shape of the space as an immutable property."""</span>
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<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_shape</span>
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<span class="nd">@property</span>
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<span class="k">def</span><span class="w"> </span><span class="nf">is_np_flattenable</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
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<span class="w"> </span><span class="sd">"""Checks whether this space can be flattened to a :class:`gymnasium.spaces.Box`."""</span>
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<span class="k">raise</span> <span class="ne">NotImplementedError</span>
|
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<div class="viewcode-block" id="Space.sample">
|
|
<a class="viewcode-back" href="../../../../api/spaces/#gymnasium.spaces.Space.sample">[docs]</a>
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|
<span class="k">def</span><span class="w"> </span><span class="nf">sample</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">mask</span><span class="p">:</span> <span class="n">Any</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">probability</span><span class="p">:</span> <span class="n">Any</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="n">T_cov</span><span class="p">:</span>
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|
<span class="w"> </span><span class="sd">"""Randomly sample an element of this space.</span>
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<span class="sd"> Can be uniform or non-uniform sampling based on boundedness of space.</span>
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|
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<span class="sd"> The binary mask and the probability mask can't be used at the same time.</span>
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|
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|
<span class="sd"> Args:</span>
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|
<span class="sd"> mask: A mask used for random sampling, expected ``dtype=np.int8`` and see sample implementation for expected shape.</span>
|
|
<span class="sd"> probability: A probability mask used for sampling according to the given probability distribution, expected ``dtype=np.float64`` and see sample implementation for expected shape.</span>
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|
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|
<span class="sd"> Returns:</span>
|
|
<span class="sd"> A sampled actions from the space</span>
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|
<span class="sd"> """</span>
|
|
<span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
|
|
|
|
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<div class="viewcode-block" id="Space.seed">
|
|
<a class="viewcode-back" href="../../../../api/spaces/#gymnasium.spaces.Space.seed">[docs]</a>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">seed</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">seed</span><span class="p">:</span> <span class="nb">int</span> <span class="o">|</span> <span class="kc">None</span> <span class="o">=</span> <span class="kc">None</span><span class="p">)</span> <span class="o">-></span> <span class="nb">int</span> <span class="o">|</span> <span class="nb">list</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">|</span> <span class="nb">dict</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="nb">int</span><span class="p">]:</span>
|
|
<span class="w"> </span><span class="sd">"""Seed the pseudorandom number generator (PRNG) of this space and, if applicable, the PRNGs of subspaces.</span>
|
|
|
|
<span class="sd"> Args:</span>
|
|
<span class="sd"> 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.</span>
|
|
|
|
<span class="sd"> Returns:</span>
|
|
<span class="sd"> The seed values used for all the PRNGs, for composite spaces this can be a tuple or dictionary of values.</span>
|
|
<span class="sd"> """</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="n">_np_random</span><span class="p">,</span> <span class="n">np_random_seed</span> <span class="o">=</span> <span class="n">seeding</span><span class="o">.</span><span class="n">np_random</span><span class="p">(</span><span class="n">seed</span><span class="p">)</span>
|
|
<span class="k">return</span> <span class="n">np_random_seed</span></div>
|
|
|
|
|
|
<div class="viewcode-block" id="Space.contains">
|
|
<a class="viewcode-back" href="../../../../api/spaces/#gymnasium.spaces.Space.contains">[docs]</a>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">contains</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""Return boolean specifying if x is a valid member of this space, equivalent to ``sample in space``."""</span>
|
|
<span class="k">raise</span> <span class="ne">NotImplementedError</span></div>
|
|
|
|
|
|
<span class="k">def</span><span class="w"> </span><span class="fm">__contains__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">Any</span><span class="p">)</span> <span class="o">-></span> <span class="nb">bool</span><span class="p">:</span>
|
|
<span class="w"> </span><span class="sd">"""Return boolean specifying if x is a valid member of this space."""</span>
|
|
<span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">contains</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
|
|
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">__setstate__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">state</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="nb">tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]]</span> <span class="o">|</span> <span class="n">Mapping</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="n">Any</span><span class="p">]):</span>
|
|
<span class="w"> </span><span class="sd">"""Used when loading a pickled space.</span>
|
|
|
|
<span class="sd"> This method was implemented explicitly to allow for loading of legacy states.</span>
|
|
|
|
<span class="sd"> Args:</span>
|
|
<span class="sd"> state: The updated state value</span>
|
|
<span class="sd"> """</span>
|
|
<span class="c1"># Don't mutate the original state</span>
|
|
<span class="n">state</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
|
|
|
|
<span class="c1"># Allow for loading of legacy states.</span>
|
|
<span class="c1"># See:</span>
|
|
<span class="c1"># https://github.com/openai/gym/pull/2397 -- shape</span>
|
|
<span class="c1"># https://github.com/openai/gym/pull/1913 -- np_random</span>
|
|
<span class="c1">#</span>
|
|
<span class="k">if</span> <span class="s2">"shape"</span> <span class="ow">in</span> <span class="n">state</span><span class="p">:</span>
|
|
<span class="n">state</span><span class="p">[</span><span class="s2">"_shape"</span><span class="p">]</span> <span class="o">=</span> <span class="n">state</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"shape"</span><span class="p">)</span>
|
|
<span class="k">del</span> <span class="n">state</span><span class="p">[</span><span class="s2">"shape"</span><span class="p">]</span>
|
|
<span class="k">if</span> <span class="s2">"np_random"</span> <span class="ow">in</span> <span class="n">state</span><span class="p">:</span>
|
|
<span class="n">state</span><span class="p">[</span><span class="s2">"_np_random"</span><span class="p">]</span> <span class="o">=</span> <span class="n">state</span><span class="p">[</span><span class="s2">"np_random"</span><span class="p">]</span>
|
|
<span class="k">del</span> <span class="n">state</span><span class="p">[</span><span class="s2">"np_random"</span><span class="p">]</span>
|
|
|
|
<span class="c1"># Update our state</span>
|
|
<span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">state</span><span class="p">)</span>
|
|
|
|
<div class="viewcode-block" id="Space.to_jsonable">
|
|
<a class="viewcode-back" href="../../../../api/spaces/#gymnasium.spaces.Space.to_jsonable">[docs]</a>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">to_jsonable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_n</span><span class="p">:</span> <span class="n">Sequence</span><span class="p">[</span><span class="n">T_cov</span><span class="p">])</span> <span class="o">-></span> <span class="nb">list</span><span class="p">[</span><span class="n">Any</span><span class="p">]:</span>
|
|
<span class="w"> </span><span class="sd">"""Convert a batch of samples from this space to a JSONable data type."""</span>
|
|
<span class="c1"># By default, assume identity is JSONable</span>
|
|
<span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="n">sample_n</span><span class="p">)</span></div>
|
|
|
|
|
|
<div class="viewcode-block" id="Space.from_jsonable">
|
|
<a class="viewcode-back" href="../../../../api/spaces/#gymnasium.spaces.Space.from_jsonable">[docs]</a>
|
|
<span class="k">def</span><span class="w"> </span><span class="nf">from_jsonable</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_n</span><span class="p">:</span> <span class="nb">list</span><span class="p">[</span><span class="n">Any</span><span class="p">])</span> <span class="o">-></span> <span class="nb">list</span><span class="p">[</span><span class="n">T_cov</span><span class="p">]:</span>
|
|
<span class="w"> </span><span class="sd">"""Convert a JSONable data type to a batch of samples from this space."""</span>
|
|
<span class="c1"># By default, assume identity is JSONable</span>
|
|
<span class="k">return</span> <span class="n">sample_n</span></div>
|
|
</div>
|
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</pre></div>
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