oops it is now 2022

Author: zkytony

CHANGELOG.rst

@@ -3,7 +3,7 @@ Changelog
 
 Best viewed on `the website <https://h2r.github.io/pomdp-py/html/changelog.html>`_.
 
-Version 1.3.2 (04/03/2021)
+Version 1.3.2 (04/03/2022)
 -------------------------
 * Fix in :py:mod:`~pomdp_py.representations.distribution.histogram` (in :code:`__str__`):
   Print all of histogram as is instead of printing top 5 to avoid confusion.

docs/html/_sources/changelog.rst.txt

@@ -3,7 +3,7 @@ Changelog
 
 Best viewed on `the website <https://h2r.github.io/pomdp-py/html/changelog.html>`_.
 
-Version 1.3.2 (04/03/2021)
+Version 1.3.2 (04/03/2022)
 -------------------------
 * Fix in :py:mod:`~pomdp_py.representations.distribution.histogram` (in :code:`__str__`):
   Print all of histogram as is instead of printing top 5 to avoid confusion.

docs/html/api/pomdp_py.algorithms.html

@@ -161,7 +161,7 @@ <h1>pomdp_py.algorithms package<a class="headerlink" href="#pomdp-py-algorithms-
 <td><p>PO-rollout: Baseline algorithm in the POMCP paper</p></td>
 </tr>
 <tr class="row-even"><td><p><a class="reference internal" href="#pomdp_py.algorithms.po_uct.POUCT" title="pomdp_py.algorithms.po_uct.POUCT"><code class="xref py py-obj docutils literal notranslate"><span class="pre">POUCT</span></code></a></p></td>
-<td><p>POUCT (Partially Observable UCT) <a class="bibtex reference internal" href="../examples.tiger.html#silver2010monte" id="id1">[1]</a> is presented in the POMCP paper as an extension of the UCT algorithm to partially-observable domains that combines MCTS and UCB1 for action selection.</p></td>
+<td><p>POUCT (Partially Observable UCT) <a class="bibtex reference internal" href="#silver2010monte" id="id1">[1]</a> is presented in the POMCP paper as an extension of the UCT algorithm to partially-observable domains that combines MCTS and UCB1 for action selection.</p></td>
 </tr>
 <tr class="row-odd"><td><p><a class="reference internal" href="#pomdp_py.algorithms.pomcp.POMCP" title="pomdp_py.algorithms.pomcp.POMCP"><code class="xref py py-obj docutils literal notranslate"><span class="pre">POMCP</span></code></a></p></td>
 <td><p>POMCP is POUCT + particle belief representation.</p></td>
@@ -178,14 +178,14 @@ <h1>pomdp_py.algorithms package<a class="headerlink" href="#pomdp-py-algorithms-
 <col style="width: 90%" />
 </colgroup>
 <tbody>
-<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>
+<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>
 <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>
 </tr>
 </tbody>
 </table>
 <section id="module-pomdp_py.algorithms.po_rollout">
 <span id="pomdp-py-algorithms-po-rollout-module"></span><h2>pomdp_py.algorithms.po_rollout module<a class="headerlink" href="#module-pomdp_py.algorithms.po_rollout" title="Permalink to this headline">¶</a></h2>
-<p>PO-rollout: Baseline algorithm in the POMCP paper <a class="bibtex reference internal" href="../examples.tiger.html#silver2010monte" id="id3">[1]</a>.</p>
+<p>PO-rollout: Baseline algorithm in the POMCP paper <a class="bibtex reference internal" href="#silver2010monte" id="id3">[1]</a>.</p>
 <p>Quote from the POMCP paper:</p>
 <blockquote>
 <div><p>To provide a performance benchmark in these cases, we evaluated the
@@ -240,7 +240,7 @@ <h1>pomdp_py.algorithms package<a class="headerlink" href="#pomdp-py-algorithms-
 <section id="module-pomdp_py.algorithms.po_uct">
 <span id="pomdp-py-algorithms-po-uct-module"></span><h2>pomdp_py.algorithms.po_uct module<a class="headerlink" href="#module-pomdp_py.algorithms.po_uct" title="Permalink to this headline">¶</a></h2>
 <p>This algorithm is PO-UCT (Partially Observable UCT). It is
-presented in the POMCP paper <a class="bibtex reference internal" href="../examples.tiger.html#silver2010monte" id="id4">[1]</a> as an extension to the UCT
+presented in the POMCP paper <a class="bibtex reference internal" href="#silver2010monte" id="id4">[1]</a> as an extension to the UCT
 algorithm <a class="bibtex reference internal" href="#kocsis2006bandit" id="id5">[3]</a> that combines MCTS and UCB1
 for action selection.</p>
 <p>In other words, this is just POMCP without particle belief,
@@ -297,7 +297,7 @@ <h1>pomdp_py.algorithms package<a class="headerlink" href="#pomdp-py-algorithms-
 <dt class="sig sig-object py" id="pomdp_py.algorithms.po_uct.POUCT">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pomdp_py.algorithms.po_uct.</span></span><span class="sig-name descname"><span class="pre">POUCT</span></span><a class="headerlink" href="#pomdp_py.algorithms.po_uct.POUCT" title="Permalink to this definition">¶</a></dt>
 <dd><p>Bases: <a class="reference internal" href="pomdp_py.framework.html#pomdp_py.framework.planner.Planner" title="pomdp_py.framework.planner.Planner"><code class="xref py py-class docutils literal notranslate"><span class="pre">pomdp_py.framework.planner.Planner</span></code></a></p>
-<p>POUCT (Partially Observable UCT) <a class="bibtex reference internal" href="../examples.tiger.html#silver2010monte" id="id9">[1]</a> is presented in the POMCP
+<p>POUCT (Partially Observable UCT) <a class="bibtex reference internal" href="#silver2010monte" id="id9">[1]</a> is presented in the POMCP
 paper as an extension of the UCT algorithm to partially-observable domains
 that combines MCTS and UCB1 for action selection.</p>
 <p>POUCT only works for problems with action space that can be enumerated.</p>
@@ -543,7 +543,7 @@ <h1>pomdp_py.algorithms package<a class="headerlink" href="#pomdp-py-algorithms-
 <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>
 <p>Implementation of the basic policy tree based value iteration as explained
 in section 4.1 of <cite>Planning and acting in partially observable stochastic
-domains</cite> <a class="bibtex reference internal" href="../index.html#kaelbling1998planning" id="id10">[7]</a></p>
+domains</cite> <a class="bibtex reference internal" href="#kaelbling1998planning" id="id10">[2]</a></p>
 <p>Warning: No pruning - the number of policy trees explodes very fast.</p>
 <dl class="py class">
 <dt class="sig sig-object py" id="pomdp_py.algorithms.value_iteration.ValueIteration">
@@ -657,7 +657,7 @@ <h2>pomdp_py.algorithms.visual.visual module<a class="headerlink" href="#pomdp-p
 <dt class="bibtex label" id="gusmao2012towards"><span class="brackets"><a class="fn-backref" href="#id8">6</a></span></dt>
 <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>
 </dd>
-<dt class="bibtex label" id="kaelbling1998planning"><span class="brackets"><a class="fn-backref" href="#id10">7</a></span></dt>
+<dt class="bibtex label" id="kaelbling1998planning"><span class="brackets"><a class="fn-backref" href="#id10">2</a></span></dt>
 <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>
 </dd>
 </dl>

docs/html/changelog.html

@@ -57,7 +57,7 @@
     <h3><a href="index.html">Table of Contents</a></h3>
     <ul>
 <li><a class="reference internal" href="#">Changelog</a><ul>
-<li><a class="reference internal" href="#version-1-3-2-04-03-2021">Version 1.3.2 (04/03/2021)</a></li>
+<li><a class="reference internal" href="#version-1-3-2-04-03-2022">Version 1.3.2 (04/03/2022)</a></li>
 <li><a class="reference internal" href="#version-1-3-1-11-03-2021">Version 1.3.1 (11/03/2021)</a></li>
 <li><a class="reference internal" href="#version-1-3-0-1-09-30-2021">Version 1.3.0.1 (09/30/2021)</a></li>
 <li><a class="reference internal" href="#version-1-2-4-6-canceled">Version 1.2.4.6 (canceled)</a></li>
@@ -84,7 +84,7 @@ <h3><a href="index.html">Table of Contents</a></h3>
 <li class="toctree-l1"><a class="reference internal" href="design_principles.html">Design Principles</a></li>
 <li class="toctree-l1"><a class="reference internal" href="existing_solvers.html">Existing POMDP Solvers</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">What's New?</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="#version-1-3-2-04-03-2021">Version 1.3.2 (04/03/2021)</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#version-1-3-2-04-03-2022">Version 1.3.2 (04/03/2022)</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#version-1-3-1-11-03-2021">Version 1.3.1 (11/03/2021)</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#version-1-3-0-1-09-30-2021">Version 1.3.0.1 (09/30/2021)</a></li>
 <li class="toctree-l2"><a class="reference internal" href="#version-1-2-4-6-canceled">Version 1.2.4.6 (canceled)</a></li>
@@ -163,8 +163,8 @@ <h3 class="donation">Donate/support</h3>
   <section id="changelog">
 <h1>Changelog<a class="headerlink" href="#changelog" title="Permalink to this headline">¶</a></h1>
 <p>Best viewed on <a class="reference external" href="https://h2r.github.io/pomdp-py/html/changelog.html">the website</a>.</p>
-<section id="version-1-3-2-04-03-2021">
-<h2>Version 1.3.2 (04/03/2021)<a class="headerlink" href="#version-1-3-2-04-03-2021" title="Permalink to this headline">¶</a></h2>
+<section id="version-1-3-2-04-03-2022">
+<h2>Version 1.3.2 (04/03/2022)<a class="headerlink" href="#version-1-3-2-04-03-2022" title="Permalink to this headline">¶</a></h2>
 <ul class="simple">
 <li><p>Fix in <a class="reference internal" href="api/pomdp_py.representations.distribution.html#module-pomdp_py.representations.distribution.histogram" title="pomdp_py.representations.distribution.histogram"><code class="xref py py-mod docutils literal notranslate"><span class="pre">histogram</span></code></a> (in <code class="code docutils literal notranslate"><span class="pre">__str__</span></code>):
 Print all of histogram as is instead of printing top 5 to avoid confusion.</p></li>

docs/html/index.html

@@ -138,7 +138,7 @@ <h1>pomdp_py Documentation<a class="headerlink" href="#pomdp-py-documentation" t
 <h2>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">¶</a></h2>
 <p><a class="reference external" href="https://github.com/h2r/pomdp-py">pomdp_py</a> is a <strong>general purpose POMDP library</strong> written in Python and Cython. It features simple and comprehensive interfaces to describe POMDP or MDP problems. Originally written to support POMDP planning research, the interfaces also allow extensions to model-free or model-based learning in (PO)MDPs, multi-agent POMDP planning/learning, and task transfer or transfer learning.</p>
 <p><strong>Why pomdp_py?</strong> It provides a POMDP framework in Python with clean and intuitive interfaces. This makes POMDP-related research or projects accessible to more people. It also helps sharing code and developing a community.</p>
-<p>POMDP stands for <strong>P</strong>artially <strong>O</strong>bservable <strong>M</strong>arkov <strong>D</strong>ecision <strong>P</strong>rocess <a class="bibtex reference internal" href="#kaelbling1998planning" id="id1">[7]</a>.</p>
+<p>POMDP stands for <strong>P</strong>artially <strong>O</strong>bservable <strong>M</strong>arkov <strong>D</strong>ecision <strong>P</strong>rocess <a class="bibtex reference internal" href="api/pomdp_py.algorithms.html#kaelbling1998planning" id="id1">[2]</a>.</p>
 <p>The code is available <a class="reference external" href="https://github.com/h2r/pomdp-py">on github</a>. We welcome contributions to this library in:</p>
 <ol class="arabic simple">
 <li><p>Implementation of additional POMDP solvers (see <a class="reference internal" href="existing_solvers.html"><span class="doc">Existing POMDP Solvers</span></a>)</p></li>
@@ -228,7 +228,7 @@ <h2>Tools<a class="headerlink" href="#tools" title="Permalink to this headline">
 <li><p><a class="reference internal" href="search.html"><span class="std std-ref">Search Page</span></a></p></li>
 </ul>
 <p id="bibtex-bibliography-index-0"><dl class="bibtex citation">
-<dt class="bibtex label" id="kaelbling1998planning"><span class="brackets"><a class="fn-backref" href="#id1">7</a></span></dt>
+<dt class="bibtex label" id="kaelbling1998planning"><span class="brackets"><a class="fn-backref" href="#id1">2</a></span></dt>
 <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>
 </dd>
 </dl>

docs/html/objects.inv

@@ -503,7 +503,7 @@ <h1>Python Module Index</h1>
      <tr class="cg-2">
        <td></td>
        <td>&#160;&#160;&#160;
-       <a href="api/pomdp_py.algorithms.bsp.html#module-pomdp_py.algorithms.bsp.blqr"><code class="xref">pomdp_py.algorithms.bsp.blqr</code></a></td><td>
+       <a href="api/pomdp_py.algorithms.html#module-pomdp_py.algorithms.bsp.blqr"><code class="xref">pomdp_py.algorithms.bsp.blqr</code></a></td><td>
        <em></em></td></tr>
      <tr class="cg-2">
        <td></td>