Minimax Q Learning

Minimax Q-learning addresses reinforcement learning problems in competitive or uncertain environments by framing the learning process as a zero-sum game between a learning agent and an adversary. Current research focuses on improving algorithm efficiency and convergence, particularly in multi-agent settings and continuous state spaces, often employing distributed optimization and factorized Q-function representations. These advancements aim to enhance the performance and scalability of reinforcement learning algorithms in complex scenarios, with applications ranging from multi-robot systems to robust control and game playing. The development of minimax-optimal algorithms with strong theoretical guarantees is a key focus, driving progress in both theoretical understanding and practical applications.