Prilagoditev AlphaZero za trostranski heksagonalni šah

This thesis adapts the AlphaZero framework for Three-Way Chess, a three-player variant defined by hexagonal geometry and complex coalition dynamics. To address the lack of software frameworks, a high-performance training ecosystem was developed for resource-constrained hardware. A transformer-based architecture incorporating relative positional embeddings was introduced to capture the board's unique spatial relationships. Methodological validation in Three-player Hex demonstrated that canonical input representations and geometric embeddings significantly enhance learning efficiency. In Three-Way Chess, the agent autonomously discovered advanced tactics but initially adopted a passive survivalist strategy to avoid drawing aggression. Refining the objective with material incentives corrected this behaviour, resulting in competitive performance against human opponents. These findings suggest that the efficacy of self-play in non-zero-sum multiplayer environments depends on the underlying game structure and may require additional fine-tuning.