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Awesome article. Does anyone know how to begin applying the AlphaZero techniques to games where information is NOT perfect? I'm trying to apply it to Scrabble.
by cdelsolar 8y ago
Awesome article. Does anyone know how to begin applying the AlphaZero techniques to games where information is NOT perfect? I'm trying to apply it to Scrabble. There hasn't been much AI research in this game and right now the best AI just uses brute force Monte Carlo with a flawed evaluation function (which doesn't take into account the state of the board at all, just points and tiles remaining on the opponent's rack). It's still good enough to beat top human experts about half the time, but I want to make something better.
Is it impossible to apply to these types of games? Every time I read about AlphaZero the articles mention that the techniques are meant for games of perfect information.
- sobellian 8y agoIf you search "UCT imperfect information" on Google, you'll turn up plenty of articles and slide decks, including one from David Silver that discusses reinforcement learning. The catch is that they're mostly dated before AlphaZero's emergence, so there's some original work involved to extend AlphaZero to this domain. This is likely something that DeepMind is working on themselves. It's possible that tweaking the search query might turn up more recent results. Good luck! EDIT - The David Silver lecture I mentioned actually mentions a Scrabble AI, Maven, which successfully applies MCTS. Here's a link: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/games.pdf http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/g...
- cdelsolar 8y agoThanks, I'll take a look. Yup, I'm aware of Maven. The one most commonly in use now is named Quackle, which uses the same techniques as Maven but has a slightly better initial evaluation function. But the evaluation function is very simple and just keeps into account score and leave. This doesn't suffice often.