Search-Aided Joint RL for Robust Lifelong Multi-Agent Path Finding
A recent paper published on arXiv (2608.05588) presents Search-Aided Joint Reinforcement Learning (SJRL) as a solution for the complexities associated with Lifelong Multi-Agent Path Finding (LMAPF) in practical warehouse environments. The authors introduce a novel model called LMAPF-R2, which integrates stringent safety and in-place rotation constraints, mirroring the conditions of automated warehouses. These constraints heighten the challenges of coordination, particularly in tight spaces. SJRL enhances neural policies through Causal PIBT, a planner that addresses collisions and communicates intentions, while also establishing a comprehensive RL framework for training agents. This research, announced as a cross-type submission, seeks to boost the efficiency and resilience of multi-agent pathfinding in dynamic settings, relevant to robotics and automated warehousing.
Key facts
- arXiv:2608.05588
- Introduces LMAPF-R2 model with robust safety and rotation constraints
- Proposes Search-Aided Joint Reinforcement Learning (SJRL)
- Uses Causal PIBT, a single-step search-based planner
- Addresses lifelong multi-agent path finding in warehouses
- Cross-type announcement on arXiv
- Focuses on coordination in constrained spaces
- Aims to improve real-world performance
Entities
—