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Earlier quoted context omitted.
That 'only' usage in the winning position could be a decisive for gaining GM rating.
Positions with 99% win percentage are not decisive for GM vs non-GM rating.
For winning any game at some point (at the end of the game) there will be a position with >99% winning chances. The move that follows are decisive.
The path to AGI: 0. Have model A. 1. Use Monte Carlo with A to get supervised data. 2. Train model B with data from A. 3. Use Monte Carlo with B to get supervised data. 4. Train model C with data from B...
https://medium.com/applied-data-science/alphago-zero-explain...
The path to AGI: 0. Have model A. 1. Use Monte Carlo with A to get supervised data. 2. Train model B with data from A. 3. Use Monte Carlo with B to get supervised data. 4. Train model C with data from B...
But the "other stuff" is pretty important. That is what pulls it away from just constantly re-amplifying the bias in the initial training data.
There is rampant misunderstanding of some parts of this article; allow me to help :) The "no-search" chess engine uses search (Stockfish) in in two ways: 1. To score positions in the training data. This is only training data, no search is performed when actually playing. 2. To play moves when the position has many options with a 99% win rate. This is to prevent pathological behavior in already won positions, and is n…
For most games, if you can see a way to an end state within 3-5 steps under these idealized conditions, there's only so much that an actual opponent can do to make the board deviate from the initial static board state that you used in your assumption. The optimal strategy will always be just a few minor corrections of edit distance from this dumb no-theory-of-mind strategy. You can always be sure that whoever has the longer path to victory has to do something to interfere with the shorter path of their opponent, and there's only ever so many pieces which can interact with that shorter path. Meaning whatever path to victory is currently shortest short circuits the search for potential moves.
There is rampant misunderstanding of some parts of this article; allow me to help :) The "no-search" chess engine uses search (Stockfish) in in two ways: 1. To score positions in the training data. This is only training data, no search is performed when actually playing. 2. To play moves when the position has many options with a 99% win rate. This is to prevent pathological behavior in already won positions, and is n…
However, in reality all positions are actually wins (for black or white) or draws. One reason they gave for why stockfish is needed to finish the game is because their evaluation function is imperfect, which is also an notable result.
Slightly off topic but am I the only one that approaches strategy games by making a "zeroth order approximation". Eg find the shortest path to victory under the (obviously faulty) assumption that my opponent does nothing and the board is unchanging except for my moves. Now find my opponents shortest path to victory under the same assumption. Then evaluate, if we both just ignore each other and try to bum rush the vic…