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/_sources/components/agents/other/dfp.rst.txt

@@ -32,8 +32,8 @@ Training the network
 Given a batch of transitions, run them through the network to get the current predictions of the future measurements
 per action, and set them as the initial targets for training the network. For each transition
 :math:`(s_t,a_t,r_t,s_{t+1} )` in the batch, the target of the network for the action that was taken, is the actual
- measurements that were seen in time-steps :math:`t+1,t+2,t+4,t+8,t+16` and :math:`t+32`.
- For the actions that were not taken, the targets are the current values.
+measurements that were seen in time-steps :math:`t+1,t+2,t+4,t+8,t+16` and :math:`t+32`.
+For the actions that were not taken, the targets are the current values.
 
 
 .. autoclass:: rl_coach.agents.dfp_agent.DFPAlgorithmParameters