MARL (Multi-Agent Reinforcement Learning)

Multi-Agent Reinforcement Learning (MARL) is a field of artificial intelligence where multiple agents (like robots or software programs) learn to make decisions and improve their actions through interactions with each other and their environment. Each agent seeks to maximize its own performance, but their success can depend on the actions of others. MARL is used in various applications, such as game playing, robotic teams, and simulations, where collaboration or competition among agents influences outcomes, leading to complex dynamics that require advanced strategies for effective learning and adaptation.