I am not a specialist of the subject, and my question is probably very naive. It stems from an essay to understand the powers and limitation of reinforcement learning as used in the AlphaGo program.
The program AlphaGo has been built using, among other things (Monte-Carlo exploration of trees, etc.), neural networks which are trained from a huge database of human-played go games, and which are then reinforced by letting play versions of the program against itself many times.
Now I wonder what would happen is we tried to build such a program without human database, i.e. starting with a basic program of Go just knowing rules and some method to explore trees, and letting play against itself to improve its neural network. Will we, after many games against itself, arrive at a program able to compete with or beat the best human players? And if so, how many games (in order of magnitude) would be needed for that? Or on the contrary, will such a program converge toward a much weaker player?
I assume that the experiment has not been made, since AlphaGo is so recent. But the answer may nevertheless be obvious to a specialist. Otherwise any educated guess will interest me.
One can also ask the same question for "simpler" games. If we use roughly the same reinforcement-learning technics used for AlphaGo, but with no use of human database, for a Chess program, would we eventually get a program able to beat the best human? And if so, how fast? Has this been tried? Or if not for Chess, what about Checkers, or even simpler games?
Thanks a lot.