Multi Agent Exploration

Multi-agent exploration focuses on developing algorithms enabling multiple robots or agents to collaboratively explore unknown environments efficiently and effectively. Current research emphasizes improving exploration strategies through techniques like intrinsic rewards, hierarchical planning (often using graph neural networks or transformer models), and efficient map representation and sharing (e.g., using topological maps or compressed representations). These advancements are crucial for applications such as planetary exploration, search and rescue, and collaborative robotics, where coordinated exploration is essential for optimizing resource utilization and task completion.