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/spaces.py

@@ -403,7 +403,8 @@ class DiscreteActionSpace(ActionSpace):
         return np.random.choice(self.actions)
 
     def sample_with_info(self) -> ActionInfo:
-        return ActionInfo(self.sample(), action_probability=1. / (self.high[0] - self.low[0] + 1))
+        return ActionInfo(self.sample(),
+                          all_action_probabilities=np.full(len(self.actions), 1. / (self.high[0] - self.low[0] + 1)))
 
     def get_description(self, action: int) -> str:
         if type(self.descriptions) == list and 0 <= action < len(self.descriptions):
@@ -450,7 +451,7 @@ class MultiSelectActionSpace(ActionSpace):
         return random.choice(self.actions)
 
     def sample_with_info(self) -> ActionInfo:
-        return ActionInfo(self.sample(), action_probability=1. / len(self.actions))
+        return ActionInfo(self.sample(), all_action_probabilities=np.full(len(self.actions), 1. / len(self.actions)))
 
     def get_description(self, action: np.ndarray) -> str:
         if np.sum(len(np.where(action == 0)[0])) + np.sum(len(np.where(action == 1)[0])) != self.shape or \