25,161
edits
Changes
no edit summary
==Network==
While [[AlphaGo]] used two disjoint networks for policy and value, [[AlphaZero]] as well as Leela Chess Zero, share a common "body" connected to disjoint policy and value "heads". The “body” consists of spatial 8x8 input planes, followed by convolutional layers with B [[Neural Networks#Residual|residual]] blocks times 3x3xF filters. BxF FxB specifies the model and size of the CNN (64x6, 128x10, 192x15, 256x20 were used).
Concerning [[Nodes per Second|nodes per second]] of the MCTS, smaller models are faster to calculate than larger models. They are faster to train and one may earlier recognize progress, but they will also saturate earlier so that at some point more training will no longer improve the engine.
Larger and deeper network models will improve the receptivity, the amount of knowledge and pattern to extract from the training samples, with potential for a [[Playing Strength|stronger]] engine.
