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Code Issues Projects Releases Wiki Activity

ACER algorithm (#184)

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* initial ACER commit

* Code cleanup + several fixes

* Q-retrace bug fix + small clean-ups

* added documentation for acer

* ACER benchmarks

* update benchmarks table

* Add nightly running of golden and trace tests. (#202)

Resolves #200

* comment out nightly trace tests until values reset.

* remove redundant observe ignore (#168)

* ensure nightly test env containers exist. (#205)

Also bump integration test timeout

* wxPython removal (#207)

Replacing wxPython with Python's Tkinter.
Also removing the option to choose multiple files as it is unused and causes errors, and fixing the load file/directory spinner.

* Create CONTRIBUTING.md (#210)

* Create CONTRIBUTING.md.  Resolves #188

* run nightly golden tests sequentially. (#217)

Should reduce resource requirements and potential CPU contention but increases
overall execution time.

* tests: added new setup configuration + test args (#211)

- added utils for future tests and conftest
- added test args

* new docs build

* golden test update
This commit is contained in:
shadiendrawis
2019-02-20 23:52:34 +02:00
committed by GitHub
parent 7253f511ed
commit 2b5d1dabe6
175 changed files with 2327 additions and 664 deletions
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6
docs/components/agents/value_optimization/double_dqn.html
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@@ -107,6 +107,7 @@
<ul class="current">
<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Agents</a><ul class="current">
<li class="toctree-l2"><a class="reference internal" href="../policy_optimization/ac.html">Actor-Critic</a></li>
<li class="toctree-l2"><a class="reference internal" href="../policy_optimization/acer.html">ACER</a></li>
<li class="toctree-l2"><a class="reference internal" href="../imitation/bc.html">Behavioral Cloning</a></li>
<li class="toctree-l2"><a class="reference internal" href="bs_dqn.html">Bootstrapped DQN</a></li>
<li class="toctree-l2"><a class="reference internal" href="categorical_dqn.html">Categorical DQN</a></li>
@@ -226,7 +227,7 @@
<h3>Training the network<a class="headerlink" href="#training-the-network" title="Permalink to this headline"></a></h3>
<ol class="arabic simple">
<li>Sample a batch of transitions from the replay buffer.</li>
<li>Using the next states from the sampled batch, run the online network in order to find the $Q$ maximizing
<li>Using the next states from the sampled batch, run the online network in order to find the <span class="math notranslate nohighlight">\(Q\)</span> maximizing
action <span class="math notranslate nohighlight">\(argmax_a Q(s_{t+1},a)\)</span>. For these actions, use the corresponding next states and run the target
network to calculate <span class="math notranslate nohighlight">\(Q(s_{t+1},argmax_a Q(s_{t+1},a))\)</span>.</li>
<li>In order to zero out the updates for the actions that were not played (resulting from zeroing the MSE loss),
@@ -286,7 +287,8 @@ Set those values as the targets for the actions that were not actually played.</
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