A Self-Trained Engine for a Chess Variant

Abstract

Chess has long been a benchmark for artificial intelligence (AI) research due to its complexity and well-defined rules. Recent advances, such as AlphaZero, introduced self-learning AI through reinforcement learning and self-play, achieving superhuman performance without prior strategic knowledge, relying solely on the rules of the game. AlphaZero defeated the world-champion chess engine Stockfish after only four hours of training, leveraging large-scale computational resources to rapidly learn and refine its strategies. This thesis presents the development of a chess engine for the chess variant Atomic Chess. The engine was developed in C++ and trained through self-play and reinforcement learning, taking inspiration from AlphaZero’s approach. This project explores the extent to which a chess engine with this approach is feasible for the average enthusiast. Cost-effective cloud-based virtual machine instances with powerful hardware were rented to manage training workloads. Given limited computational resources, we opted for a data-centric approach, focusing on refining the training pipeline to maximize the training data that could be produced, rather than hyperparameter tuning and experimenting with neural network architectures. The final engine was trained on approximately 450,000 self-play games in roughly 150 hours. The final engine was deployed on the chess platform Lichess and achieved an ELOrating of 1,729, which corresponded to the top 10th percentile of Atomic Chess players on Lichess. These results demonstrate that it is possible to achieve a competitive Atomic Chess engine within a budget of 3,000 SEK for cloud computation. This shows that strong self-play reinforcement learning agents for niche games can be developed without requiring large-scale computing infrastructure. These results highlight the viability of accessible, low-budget AI research for underexplored game variants.