- saving monte carlo did not include returnSum and returnCount, so it the state would be wrong after loading. Learning, EpisodicLearning and MonteCarlo classes are all overriding custom save and load methods, calling super() each time but including fields that are necessary to replace on runtime.
- moved generic episodic behaviour from monteCarlo to abstract top level class
- using AtomicInteger for episodesToLearn
- moved learning-Thread-handling from controller to model. Learning got one extra Leaning thread.
- add feature to use custom speed and distance for dino world obstacles
- add feature to save and load learning progress (Q-Table) and current episode count
- episode end is now purely decided by environment instead of monte carlo algo capping it on 10 actions
- using linkedHashMap on all locations to ensure deterministic behaviour
- fixed major RNG issue to reproduce algorithmic behaviour
- clearing rewardHistory, to only save the last 10k rewards
- added google dino jump environment
Jan Löwenstrom