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How would this handle games with random or incomplete information? Such as UNO, craps, etc. (I'd love to see what this thing does with a known losing game, ju
by ilc 3y ago
How would this handle games with random or incomplete information? Such as UNO, craps, etc. (I'd love to see what this thing does with a known losing game, just as a validation.)
- s-casci 3y agoAlphaZero has been made for perfect information games. That said, the Monte Carlo Tree Search in the library can be run with any agent that implements a value and policy function. So, while the AlphaZeroAgent in agents.py wouldn't fit the problem you are describing, implementing something like Meta's ReBeL (https://ai.meta.com/blog/rebel-a-general-game-playing-ai-bot-that-excels-at-poker-and-more/ https://ai.meta.com/blog/rebel-a-general-game-playing-ai-bot...) shouldn't be an impossible task. The Monte Carlo Tree Search algorithm in mcts.py has been written to be modular from the start exactly to do something like this!
- gwern 3y agoThe standard AlphaZero doesn't handle that. For that you'd need to graduate to more complex variants like the aforementioned ReBeL, AlphaZe* https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213697/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10213697/ or BetaZero https://arxiv.org/abs/2306.00249 https://arxiv.org/abs/2306.00249 or ExIt-OOS https://arxiv.org/abs/1808.10120 https://arxiv.org/abs/1808.10120 or Player of Games https://arxiv.org/abs/2112.03178#deepmind https://arxiv.org/abs/2112.03178#deepmind . (You could also move straight to MuZero variations: https://arxiv.org/abs/2106.04615#deepmind https://arxiv.org/abs/2106.04615#deepmind https://openreview.net/forum?id=X6D9bAHhBQ1#deepmind https://openreview.net/forum?id=X6D9bAHhBQ1#deepmind https://openreview.net/forum?id=QnzSSoqmAvB https://openreview.net/forum?id=QnzSSoqmAvB )