Reinforcement Learning (RL) and Game Theory have emerged as pivotal methodologies in advancing intelligent robotic systems. Unlike traditional robotics, which relies heavily on deterministic programming and predefined behaviors, modern approaches increasingly utilize RL and game-theoretic principles to enable autonomous systems to learn, adapt, and collaborate in dynamic, uncertain, and adversarial environments .