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

rl_coach/agents/policy_optimization_agent.py

@@ -149,7 +149,7 @@ class PolicyOptimizationAgent(Agent):
             action_probabilities = np.array(action_values).squeeze()
             action = self.exploration_policy.get_action(action_probabilities)
             action_info = ActionInfo(action=action,
-                                     action_probability=action_probabilities[action])
+                                     all_action_probabilities=action_probabilities)
 
             self.entropy.add_sample(-np.sum(action_probabilities * np.log(action_probabilities + eps)))
         elif isinstance(self.spaces.action, BoxActionSpace):