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‎docs/html/_modules/index.html

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<head>
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<meta charset="utf-8" />
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<meta name="viewport" content="width=device-width, initial-scale=1.0" />
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<title>Overview: module code &#8212; pomdp_py 1.3.1 documentation</title>
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<title>Overview: module code &#8212; pomdp_py 1.3.2 documentation</title>
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<p class="blurb">A framework to build and solve POMDP problems (v1.3.1).</p>
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<p class="blurb">A framework to build and solve POMDP problems (v1.3.2).</p>
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‎docs/html/_sources/changelog.rst.txt

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Version 1.3.2 (04/03/2021)
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-------------------------
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* Fix in :py:mod:`~pomdp_py.representations.distribution.particles` (in :code:`__str__`):
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* Fix in :py:mod:`~pomdp_py.representations.distribution.histogram` (in :code:`__str__`):
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Print all of histogram as is instead of printing top 5 to avoid confusion.
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* Improve documentation for `the tiger tutorial <https://h2r.github.io/pomdp-py/html/examples.tiger.html>`_;
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Specifically, clarified :py:mod:`~pomdp_py.framework.basics.PolicyModel`

‎docs/html/_static/documentation_options.js

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VERSION: '1.3.1',
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‎docs/html/api/modules.html

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<title>pomdp_py &#8212; pomdp_py 1.3.1 documentation</title>
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<title>pomdp_py &#8212; pomdp_py 1.3.2 documentation</title>
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<p class="blurb">A framework to build and solve POMDP problems (v1.3.1).</p>
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<p class="blurb">A framework to build and solve POMDP problems (v1.3.2).</p>
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‎docs/html/api/pomdp_py.algorithms.bsp.html

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<title>pomdp_py.algorithms.bsp package &#8212; pomdp_py 1.3.2 documentation</title>
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<p class="blurb">A framework to build and solve POMDP problems (v1.3.1).</p>
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<p class="blurb">A framework to build and solve POMDP problems (v1.3.2).</p>
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‎docs/html/api/pomdp_py.algorithms.html

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<title>pomdp_py.algorithms package &#8212; pomdp_py 1.3.1 documentation</title>
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<title>pomdp_py.algorithms package &#8212; pomdp_py 1.3.2 documentation</title>
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<p class="blurb">A framework to build and solve POMDP problems (v1.3.1).</p>
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<p class="blurb">A framework to build and solve POMDP problems (v1.3.2).</p>
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<tr class="row-odd"><td><p><a class="reference internal" href="#module-pomdp_py.algorithms.bsp.blqr" title="pomdp_py.algorithms.bsp.blqr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">blqr</span></code></a></p></td>
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<tr class="row-odd"><td><p><a class="reference internal" href="pomdp_py.algorithms.bsp.html#module-pomdp_py.algorithms.bsp.blqr" title="pomdp_py.algorithms.bsp.blqr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">blqr</span></code></a></p></td>
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<td><p>Implementation of B-LQR algorithm described in &quot;Belief space planning assuming maximum likelihood observations&quot; <a class="bibtex reference internal" href="#platt2010belief" id="id2">[2]</a></p></td>
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<span id="pomdp-py-algorithms-value-iteration-module"></span><h2>pomdp_py.algorithms.value_iteration module<a class="headerlink" href="#module-pomdp_py.algorithms.value_iteration" title="Permalink to this headline"></a></h2>
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<p>Implementation of the basic policy tree based value iteration as explained
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in section 4.1 of <cite>Planning and acting in partially observable stochastic
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domains</cite> <a class="bibtex reference internal" href="../examples.tiger.html#kaelbling1998planning" id="id10">[1]</a></p>
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domains</cite> <a class="bibtex reference internal" href="../index.html#kaelbling1998planning" id="id10">[7]</a></p>
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<p>Warning: No pruning - the number of policy trees explodes very fast.</p>
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<dt class="bibtex label" id="gusmao2012towards"><span class="brackets"><a class="fn-backref" href="#id8">6</a></span></dt>
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<dd><p>António Gusmao and Tapani Raiko. Towards generalizing the success of monte-carlo tree search beyond the game of go. In <em>ECAI</em>, 384–389. 2012.</p>
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<dt class="bibtex label" id="kaelbling1998planning"><span class="brackets"><a class="fn-backref" href="#id10">1</a></span></dt>
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<dt class="bibtex label" id="kaelbling1998planning"><span class="brackets"><a class="fn-backref" href="#id10">7</a></span></dt>
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<dd><p>Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra. Planning and acting in partially observable stochastic domains. <em>Artificial intelligence</em>, 101(1-2):99–134, 1998.</p>
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‎docs/html/api/pomdp_py.algorithms.visual.html

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‎docs/html/api/pomdp_py.framework.html

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‎docs/html/api/pomdp_py.representations.belief.html

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<title>pomdp_py.representations.belief package &#8212; pomdp_py 1.3.2 documentation</title>
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