Multi Agent Navigation

Multi-agent navigation focuses on enabling multiple robots or agents to move efficiently and safely within a shared environment, avoiding collisions while reaching their individual or collective goals. Current research emphasizes decentralized approaches using deep reinforcement learning (DRL), graph neural networks (GNNs), and hierarchical planning architectures to handle complex interactions and large numbers of agents, often incorporating communication strategies and environment optimization. These advancements are crucial for improving the scalability and robustness of autonomous systems in various applications, such as robotics, air traffic control, and swarm robotics.