Multi-Agent Reinforcement Learning

Multi-Agent Reinforcement Learning (MARL) involves multiple intelligent systems (agents) that learn by interacting within a shared environment. Each agent makes decisions based on its observations and aims to achieve its goals, which may involve cooperation or competition with others. Through trial and error, they adapt their strategies to optimize their outcomes, considering how others might react. MARL is used in complex scenarios like robotics, game playing, and traffic management, enabling systems to learn optimal behaviors collectively and adapt dynamically to changing conditions.