HiveMind: Learning to Play the Cooperative Chess Variant Bughouse with DNNs and MCTS.

In 2017, the AlphaZero algorithm achieved superhuman performance in chess, outperforming the then-reigning chess engine Stockfish 8. AlphaZero has since been applied to other variants of chess, such as Crazyhouse, with similarly impressive results. However, limited work has been done on the chess variant Bughouse, which has both cooperative and real-time aspects, as well as a far higher game tree complexity than chess. In this paper, we present HiveMind, a neural network Bughouse engine that focuses on cooperation and decision-making. We trained HiveMind via supervised learning on human Bughouse games. We then used the AlphaZero algorithm by incorporating domain knowledge, using clock times to determine the optimal turn sequence, to perform a tree search over both boards. This two-board search incorporated all aspects of Bughouse, including being time aware and capable of cross-board coordination without heuristics. Finally, we evaluated the strength of HiveMind by playing matches with different time settings against Fairy-Stockfish, the current state-of-the-art alpha-beta Bughouse engine. HiveMind convincingly defeated Fairy-Stockfish achieving a win rate of over 95% with a search time of 2 seconds, showing significantly better scaling.