ACER algorithm (#184)

* 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

docs_raw/source/selecting_an_algorithm.rst

@@ -190,6 +190,14 @@ The algorithms are ordered by their release date in descending order.
                learning stability and speed, both for discrete and continuous action spaces.
             </span>
          </div>
+         <div class="algorithm discrete on-policy requires-multi-worker" data-year="201707">
+            <span class="badge">
+               <a href="components/agents/policy_optimization/acer.html">ACER</a>
+               <br>
+               Similar to A3C with the addition of experience replay and off-policy training. to reduce variance and
+               improve stability it also employs bias correction and trust region optimization techniques.
+            </span>
+         </div>
          <div class="algorithm continuous off-policy" data-year="201509">
             <span class="badge">
                <a href="components/agents/policy_optimization/ddpg.html">DDPG</a>