4 Commits

Author	SHA1	Message	Date
Jan Löwenstrom	5a4e380faf	add dino jumping environment, deterministic/reproducable behaviour and save-and-load feature ... - 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	2019-12-22 23:33:56 +01:00
Jan Löwenstrom	34e7e3fdd6	distinguish learning and episodic learning, enable fast-learning without drawing every step to reduce lag ... - repainting every step on no time delay will certainly freeze the app, so "fast-learning" will disable it, only refreshing current episode label - Added new abstract class "Episodic Learning". Maybe just use an interface instead?! Important because TD learning is not episodic, needs another way to represent the rewards received (maybe mean of last X rewards or sth) - Opening two JFrames, one with learning infos and one with environment	2019-12-21 00:23:09 +01:00
Jan Löwenstrom	c11cc2c3f2	add two simple scroll panes to represent environment and ant brain	2019-12-09 01:09:34 +01:00
Jan Löwenstrom	87f435c65a	add basic core structure and first parts of antGame implementation	2019-12-07 22:05:11 +01:00