Transfer Learning in Monte Carlo Tree Search
Nathan Cleaver, Kourosh Neshatian · 2023
Transfer learning is the ability to transfer knowledge from one context to another. This paper investigates, for the first time, the possibility of transfer learning on Monte Carlo Tree Search (MCTS). We use distributed parallel MCTS to construct game-playing models for one variant of chess and then transfer these models to another domain to play a different variant of chess. We also introduce a novel pruning mechanism for MCTS to cope with memory requirements of large game trees. Our results show that positive transfer learning in MCTS is possible, and in our case, highly successful. Transferred models significantly outperform models that have to be trained from scratch.