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What's very interesting is that the Komodo developers have implemented a Monte Carlo Tree Search version of their engine without neural nets for evaluation / mo
by stabbles 8y ago
What's very interesting is that the Komodo developers have implemented a Monte Carlo Tree Search version of their engine without neural nets for evaluation / move selection. This brand new engine can actually compete at the top level (still much worse than Stockfish and slightly worse than Lc0) [1] [2]
The exact implementation details are probably kept secret, but the idea is to do a few steps of minimax / alpha-beta rather than completely random play in the playout phase of MCTS.
This makes me think that the contribution of AlphaZero is not necessarily neural nets, but rather MCTS as a succesful method to search the game tree efficiently.
[1] http://tcec.chessdom.com/ http://tcec.chessdom.com/
[2] http://www.chessdom.com/komodo-mcts-monte-carlo-tree-search-is-the-new-star-of-tcec/ http://www.chessdom.com/komodo-mcts-monte-carlo-tree-search-...
- ggggtez 8y agoYou missed the point then. Alpha beta pruning requires knowledge of the game rules. Neural network pruning doesn't. The advantage is that it's a general purpose technique.
- stabbles 8y agoYes, that's the main contribution of the experiment / paper. But prior to AlphaZero the chess community did not even consider investing in MCTS engines -- alpha-beta pruning was thought to be far superior. I'm thinking that we might see classical engines exploring this concept more, and maybe it's even a natural step to go from alpha-beta pruning + iterative deepening to 'best-first' search with MCTS.