Decentralized Planning

Decentralized planning focuses on coordinating multiple agents or systems without a central controller, aiming for efficient resource allocation and robust performance even with agent failures or communication limitations. Current research emphasizes algorithms like distributed ADMM, multi-agent MCTS, and RRT variants, often incorporating deep reinforcement learning or graph neural networks to handle complex environments and dynamic situations. This approach is crucial for applications ranging from wildfire mitigation and warehouse robotics to traffic management and space exploration, offering scalability and resilience superior to centralized methods in many scenarios.