Multi Agent Reinforcement Learning

Multi-agent reinforcement learning (MARL) focuses on developing algorithms that enable multiple independent agents to learn optimal strategies within a shared environment, often to achieve a common goal. Current research emphasizes improving sample efficiency and generalization, exploring novel architectures like equivariant graph neural networks and specialized network structures (e.g., Bottom-Up Networks), and addressing challenges posed by non-stationarity and partial observability through techniques such as auxiliary prioritization and global state inference with diffusion models. MARL's significance lies in its potential to solve complex real-world problems across diverse domains, including robotics, traffic control, and healthcare, by enabling effective coordination and collaboration among multiple agents.