12-01-2019, 06:42 PM
For now, we've been focusing on simpler linear models. Some of the papers in the list of our project's outputs (http://www.ludeme.eu/outputs/) describe some of the stuff we've been doing with self-play training. Those papers so far have all been about learning policies (to be incorporated in MCTS) agents though, not so much about learning state-value functions (for MCTS) or state evaluation functions for alpha-beta.
NNs are a little bit problematic still for a few reasons. The primary one is that we're not working with Google-levels of hardware, and we're trying to learn to play hundreds of games rather than ~3 games. For our project, we also don't really need superhuman AI though, we're aiming for "average-game-enthusiast" levels of skill mostly.
Good idea, thanks, we'll keep it in mind!
NNs are a little bit problematic still for a few reasons. The primary one is that we're not working with Google-levels of hardware, and we're trying to learn to play hundreds of games rather than ~3 games. For our project, we also don't really need superhuman AI though, we're aiming for "average-game-enthusiast" levels of skill mostly.
Quote:Might I suggest making it possible to write custom evaluators as a simpler alternative to writing custom selectAction functions.
Good idea, thanks, we'll keep it in mind!