Multi-Agent Q-Learning

Multi-Agent Q-Learning is an extension of traditional Q-learning where multiple agents learn simultaneously within a shared environment. Each agent aims to maximize its own rewards by choosing actions based on a knowledge table (Q-values). Unlike single-agent scenarios, agents must consider not only the environment’s state but also the actions of other agents, which influence outcomes. Through exploration and feedback, agents iteratively update their Q-values to improve decision-making, often leading to coordinated strategies or competitive behaviors. This approach is used in complex systems like robotics, game-playing, or traffic management, where multiple autonomous entities learn and adapt concurrently.