Online Planning

Online planning addresses the challenge of making optimal decisions in dynamic, uncertain environments, aiming to efficiently find solutions in real-time. Current research focuses on improving the scalability and efficiency of algorithms like Monte Carlo Tree Search (MCTS) and its variants, particularly for multi-agent systems and continuous state/action spaces, often incorporating techniques like distributed optimization and factored representations. These advancements are crucial for applications ranging from autonomous robotics (e.g., drone racing, aerial surveillance) to multi-robot coordination and resource management, enabling more robust and adaptable intelligent systems.