MARL Algorithm

Multi-agent reinforcement learning (MARL) focuses on training multiple agents to cooperate or compete within a shared environment, aiming to optimize collective or individual performance. Current research emphasizes addressing challenges like non-stationarity (agents changing the environment for each other), improving safety and robustness through methods such as bilevel optimization and certified policy smoothing, and enhancing coordination and communication, including the exploration of emergent communication and hierarchical strategies. These advancements are crucial for deploying MARL in real-world applications like autonomous driving and robotics, where safe and efficient multi-agent collaboration is essential.