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Integration in MCTS should be straightforward. And I would be surprised if the author's hadn't done it already. Normally, no actions are pruned away per-se. Ins

by jpfr 12y ago

Integration in MCTS should be straightforward. And I would be surprised if the author's hadn't done it already. Normally, no actions are pruned away per-se. Instead, the available actions are initialised with a utility value [1] from an external oracle, i.e. the neural net. From then on, the normal MCTS procedure continues until the time or memory runs out. [1] ..and a faked visit-count. So the algorithm beliefs that the action was already evaluated n times, which resulted in the given mean utility.

xxxaaa 12y ago
From the paper: " The most obvious next step is to integrate a DCNN into a full fledged Go playing system. For example, a DCNN could be run on a GPU in parallel with a MCTS Go program and be used to provide highly quality priors for what the strongest moves to consider are. Such a system would both be the first to bring sophisticated pattern recognitions abilities to playing Go, and have a strong potential ability to surpass current computer Go programs. "
gamegoblin 12y ago
I don't think they've done it yet -- in the conclusion they say: The most obvious next step is to integrate a DCNN into a full fledged Go playing system. For example, a DCNN could be run on a GPU in parallel with a MCTS Go program and be used to provide highly quality priors for what the strongest moves to consider are. Such a system would both be the first to bring sophisticated pat- tern recognitions abilities to playing Go, and have a strong potential ability to surpass current computer Go programs. I agree that the integration should be exceedingly straightforward. I've written MCTS implementations (though not a Go implementation -- I used it on Connect 4 and other easy-to-code games), and it seems like you'd just plug it into your already-existing bias function. The authors didn't mention my idea about using the probability distribution output from the network to guide the random playouts, which I would also be interested in.

by jpfr 12y ago

xxxaaa 12y ago

From the paper: " The most obvious next step is to integrate a DCNN into a full fledged Go playing system. For example, a DCNN could be run on a GPU in parallel with a MCTS Go program and be used to provide highly quality priors for what the strongest moves to consider are. Such a system would both be the first to bring sophisticated pattern recognitions abilities to playing Go, and have a strong potential ability to surpass current computer Go programs. "

gamegoblin 12y ago

I don't think they've done it yet -- in the conclusion they say: The most obvious next step is to integrate a DCNN into a full fledged Go playing system. For example, a DCNN could be run on a GPU in parallel with a MCTS Go program and be used to provide highly quality priors for what the strongest moves to consider are. Such a system would both be the first to bring sophisticated pat- tern recognitions abilities to playing Go, and have a strong potential ability to surpass current computer Go programs. I agree that the integration should be exceedingly straightforward. I've written MCTS implementations (though not a Go implementation -- I used it on Connect 4 and other easy-to-code games), and it seems like you'd just plug it into your already-existing bias function. The authors didn't mention my idea about using the probability distribution output from the network to guide the random playouts, which I would also be interested in.