Stockfish chess wiki2/20/2023 ![]() ![]() In this case it is crucial to find the most relevant subset of the tree to explore it, so spending more time on your policy makes sense.Īlpha-beta pruning however always explores the entire searchtree systematically (up to a certain depth using itd) and prunes the searchtree by discarding bad moves. MCTS tries to deal with an explosion in search-space by only sampling very small parts of the searchspace and relying on a very good heuristic to guid that search process. It's also different because Stockfish uses Alpha-beta treesearch instead of MCTS: The incremental updates are also related to Zobrist Hashing, which the Stockfish authors are certainly aware of. The depth and branching factor in chess and Go are different, so I won't say the solutions ought to be the same, but it's interesting nonetheless to see the original AlphaGo ideas be resurrected in this form. The RL-enhanced policy network was discarded in favor of training the policy network to directly replicate MCTS search statistics. The independently trained value network was discarded because co-training a value and policy head on a shared trunk saved a significant amount of compute, and helped regularize both objectives against each other. ![]() The cheap rollout policy network was discarded because DeepMind found that a "slow evaluations of the right positions" was better than "rapid evaluations of questionable positions". a cheap policy network trained on human pro games, used only for rapid rollout simulations. a value network trained on games generated by the RL-enhanced policy network a RL-enhanced policy network improving on the original SL-trained policy network. a policy network trained on human pro games. In particular, the AlphaGo paper mentioned four neural networks of significance: Ironically, a lot of the tricks Stockfish is using here are reminiscent of tricks that were used in the original AlphaGo and later discarded in AlphaGoZero.
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