Re: current development

[Timothy Y. Chow]

On Thu, 5 Dec 2019, Nikos Papachristou wrote:
> 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.

In order to determine whether a new network is doing better than the old 
network, it helps to have examples of positions where the old network is 
clearly playing poorly.  Here's one example of a game that I played 
against eXtreme Gammon where the bot made a lot of obvious blunders:

http://timothychow.net/cg/Games/7pt2015-05-24e%20Game%202.htm

For example, search for "10/8 6/4(3)".  The bot's ridiculous play here 
would not be among the top 50 plays of any halfway decent human player. 
Admittedly this was XG but I would expect GNU to behave similarly, if not 
in these specific positions then in similar ones.

Playing around with positions like this will quickly disabuse anyone of 
the illusion that "backgammon bots are very close to perfection."

As I recall, in the past, people have tried specifically training neural 
nets on positions like these, as well as "snake" positions where you have 
to roll a prime for a long distance, and the problem was that it seemed to 
degrade performance on other types of positions.  It's possible that, as 
Papachristou suggests, recent incremental improvements in regularization 
algorithms might be good enough to overcome these difficulties.  Anecdotal 
evidence from Robert Wachtel's revised version of "In the Game Until the 
End" suggests that Xavier was able to improve eXtreme Gammon's post-coup 
classique play significantly, without a wholesale switch to modern deep 
learning methods.

Tim