02-24-2022, 10:06 AM
(02-23-2022, 02:14 PM)Michael Wrote: Let's say I there's a Ludii game for which my laptop is only able to generate fewer than 2 playouts per second.. Is there any hope of learning something about the game from AI agents? Or do I need a different (non-Ludii) implementation of such a game to get anywhere?
I don't know anything about these kinds of things, but I'm learning to code at the moment, so I might be able to learn different technology for this purpose, I just don't have any idea where to begin.. Any pointers?
I imagine that an algorithm that heavily relies on those playouts (like a standard MCTS) would be fairly hopeless in such a scenario.
Algorithms that might be promising in such a case could be:
1) Minimax/AlphaBeta/other variants, which don't use (presumably deep, long and expensive) playouts for evaluations.
2) Variants of MCTS that don't use (long) playouts, but instead truncate them early (or maybe don't run them at all) and have different mechanisms for backing up values.
These have other downsides though. Like, Minimax/Alphabeta require (game-specific) heuristics. MCTS variants that truncate playouts early also need heuristics, or things like Deep Neural Networks, to backpropagate value estimates. Heuristics are generally game-specific and therefore annoying, whereas Deep Neural Networks require huge amounts of compute to get them trained initially.