Hi everybody! You can view my research publications on backgammon variants at my website: https://nikpapa.com , or alternatively you can download my PhD thesis from: https://www.didaktorika.gr/eadd/handle/10442/43622?locale=en
My personal view on improving GNUBG: Why not try to "upgrade" your existing supervised learning approach? There have been lots of advances in optimization/regularization algorithms for neural networks in the past years and it might be less demanding that trying a new RL self-play approach from scratch. Regarding expected results, I also believe that backgammon bots are very close to perfection and whatever improvements (from any approach) will be marginal. On Thu, Dec 5, 2019 at 12:14 AM Joseph Heled <[email protected]> wrote: > A link to something? article? software? did they use alpha-like strategies? > > -Joseph > > On Thu, 5 Dec 2019 at 11:04, Philippe Michel <[email protected]> > wrote: > >> On Wed, Dec 04, 2019 at 02:07:18PM -0500, Timothy Y. Chow wrote: >> >> > Also, it's my impression that many people *don't* think this is even a >> > worthwhile idea to pursue. Backgammon is already "solved," is what >> they >> > will say. It's true that "AlphaGammon" will surely not crush existing >> > bots in a series of (say) 11-point matches. At most I would expect a >> > slight advantage. But to me, that is the wrong way to look at the >> issue. >> > I would like to understand superbackgames for their own sake, even >> though >> > they arise rarely in practice. Furthermore, if we know that bots don't >> > understand superbackgames, then the closer a position gets to being a >> > superbackgame, the less we can trust the bot verdict. >> >> I'm not sure how related it may be, but there is a group of Greek >> academics that have published some articles on their work on a bot, >> Palamedes, that plays backgammon but also variants that have different >> rules and starting positions and lead to positions that would be very >> uncommon in backgammon. >> >> >> >>
