[Discussion] Fragility of AlphaZero vs Stockfish in chess-Domain adaptation.

In terms of transfer learning/domain adaptation for these models I was wondering how you think minor changes to the game might affect the performance?

Stockfish is a simulation based engine, while AlphaZero is the reinforcement learning engine. In a trained state, AlphaZero completely dominates Stockfish.

Assuming both are trained on a regular chess set, suppose you change the starting squares of bishops and knights, would this completely mess up AlphaZero?

I don't see it causing a huge problem for Stockfish …

alphazero chess domain adaptation machinelearning