docs html update

Author: zkytony

docs/_sphinx_src/conf.py

@@ -102,3 +102,6 @@
 
 # Do not sort automodule classes alphebatically but by how they appear in source.
 autodoc_member_order = 'bysource'
+
+# bibtex file
+bibtex_bibfiles = ['refs.bib']

docs/html/_sources/examples.tiger.rst.txt

@@ -386,7 +386,7 @@ For the Tiger problem, we implemented this procedure as follows:
             print("True state:", tiger_problem.env.state)
             print("Belief:", tiger_problem.agent.cur_belief)
             print("Action:", action)
-            # Step 3; no transition since actions in Tiger problem
+            # Step 3; There is no state transition for the tiger domain.
             # In general, the ennvironment state can be transitioned
             # using
             #
@@ -402,10 +402,9 @@ For the Tiger problem, we implemented this procedure as follows:
 
             # Step 4
             # Let's create some simulated real observation;
-            # Update the belief Creating true observation for
-            # sanity checking solver behavior. In general, this
-            # observation should be sampled from agent's observation
-            # model, as
+            # Here, we use observation based on true state for sanity
+            # checking solver behavior. In general, this observation
+            # should be sampled from agent's observation model, as
             #
             #    real_observation = tiger_problem.agent.observation_model.sample(tiger_problem.env.state, action)
             #
@@ -416,6 +415,8 @@ For the Tiger problem, we implemented this procedure as follows:
             print(">> Observation: %s" % real_observation)
 
             # Step 5
+            # Update the belief. If the planner is POMCP, planner.update
+            # also automatically updates agent belief.
             tiger_problem.agent.update_history(action, real_observation)
             planner.update(tiger_problem.agent, action, real_observation)
             if isinstance(planner, pomdp_py.POUCT):

docs/html/api/pomdp_py.algorithms.bsp.html

@@ -136,7 +136,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <section id="module-pomdp_py.algorithms.bsp.blqr">
 <span id="pomdp-py-algorithms-bsp-blqr-module"></span><h2>pomdp_py.algorithms.bsp.blqr module<a class="headerlink" href="#module-pomdp_py.algorithms.bsp.blqr" title="Permalink to this headline">¶</a></h2>
 <p>Implementation of B-LQR algorithm described in “Belief space planning
-assuming maximum likelihood observations” <a class="bibtex reference internal" href="pomdp_py.algorithms.html#platt2010belief" id="id1">[2]</a></p>
+assuming maximum likelihood observations” <span id="id1">[<a class="reference internal" href="pomdp_py.algorithms.html#id43" title="R. Platt, R. Tedrake, L. Kaelbling, and T. Lozano-Perez. Belief space planning assuming maximum likelihood observations. In Proceedings of Robotics: Science and Systems. Zaragoza, Spain, June 2010. doi:10.15607/RSS.2010.VI.037.">4</a>]</span></p>
 <dl class="py class">
 <dt class="sig sig-object py" id="pomdp_py.algorithms.bsp.blqr.BLQR">
 <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">pomdp_py.algorithms.bsp.blqr.</span></span><span class="sig-name descname"><span class="pre">BLQR</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">func_sysd</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">func_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jac_sysd</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jac_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">jac_sysd_u</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">noise_sysd</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Qlarge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">L</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Q</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">R</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">planning_horizon</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">15</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/pomdp_py/algorithms/bsp/blqr.html#BLQR"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#pomdp_py.algorithms.bsp.blqr.BLQR" title="Permalink to this definition">¶</a></dt>