MARL Algorithm

MARL, or Multi-Agent Reinforcement Learning, involves multiple agents learning to make decisions within the same environment. Each agent aims to achieve its objectives while interacting with others, often adapting strategies based on shared experiences. Think of it like a team of players in a game, who gradually learn the best moves through trial, error, and observing others. This approach enables complex, dynamic systems to function efficiently—such as autonomous vehicles coordinating traffic flow or robots collaborating on tasks—by allowing each agent to learn and improve through continuous interaction and feedback.