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> What about games that can't quite be modeled with an alternating turn-based game tree like bughouse? Train a network which predicts future state of the game,
by red75prime 9y ago
> What about games that can't quite be modeled with an alternating turn-based game tree like bughouse?
Train a network which predicts future state of the game, given current state and input. Train a network which generates sensible inputs, given current state. Use MCTS.
- sobellian 9y agoBughouse, starcraft, and other important games need to be modeled as simultaneous-decision games. Plain-vanilla MCTS is designed for alternating-decision games. To see why this is important, consider why min-max (which MCTS approximates) actually works. At any given point, the equilibrium strategy for the player to move is the move that maximizes their payoff, and the utility for each move can be found recursively. In simultaneous decision games, calculating the equilibrium strategy (which may even be a mixed strategy) is more complicated. See http://mlanctot.info/files/papers/cig14-smmctsggp.pdf http://mlanctot.info/files/papers/cig14-smmctsggp.pdf for various ways in which MCTS can be extended to simultaneous-decision games. It'll be interesting to see if DeepMind picks up a search algorithm someone else has researched, or if they come up with something entirely new.
- red75prime 9y agoThanks for the link. The thing I described roughly corresponds to SUCT. It's interesting how NN will be able to deal with uncertainty of enemy's state and moves.