MARL Benchmarking

MARL (Multi-Agent Reinforcement Learning) Benchmarking is a process used to evaluate and compare different algorithms that enable multiple AI agents to learn and work together in complex environments. It provides standardized tests and metrics to measure how well these algorithms perform in tasks like cooperation, competition, and adaptation. Benchmarking helps researchers understand which methods are most effective, identify strengths and weaknesses, and drive advancements in multi-agent AI systems used in areas like robotics, gaming, and traffic management.