Reconnaissance Blind Chess

Reconnaissance Blind Chess (RBC) research focuses on developing AI agents capable of playing this imperfect-information game effectively, where players have limited knowledge of the opponent's pieces. Current research emphasizes using neural networks, particularly Siamese networks, to weigh the likelihood of different possible game states given limited observations, and applying reinforcement learning techniques like Proximal Policy Optimization to improve agent performance through self-play. These advancements contribute to a broader understanding of AI in imperfect-information games and offer insights into developing robust strategies for handling uncertainty in complex decision-making scenarios.