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A custom MARL (multi-agent reinforcement learning) environment where multiple agents trade against one another (self-play) in a zero-sum continuous double auction. Ray [RLlib] is used for training.
Desktop Reversi game made in Kotlin and Swing. The project focuses on implementing and exploring AI algorithms for zero-sum games, including MinMax, Alpha-Beta pruning, and various heuristics.