Tell me the precise code, that says nothing to me.Daniel Shawul wrote:What is different is that alphazero's evaluation selects features of eval by itself (via a nerual network), while in the standard approach the programmer select features (e.g. passsed pawns, king safety, rook on open file etc) and just tunes the weights. The downside of the neural-network approach is that you may not understand why it does what it does.
Daniel
How does it select features, based on what?
Playing 100 000 games, many wins with pawn on d4 or e5, so this is good, or interpreting 100 000 games from some large himan database, e5 pawn is more common in winnig games then d5 pawn, so increase its value.
But that has its limits.
What about mobility, how they figure out mobility from human games?
More importantly, what would an evaluation pattern consist of?