  Multi-Agent Path Finding (MAPF) is a critical component of logistics and
warehouse management, which focuses on planning collision-free paths for a team
of robots in a known environment. Recent work introduced a novel MAPF approach,
LNS2, which proposed to repair a quickly-obtainable set of infeasible paths via
iterative re-planning, by relying on a fast, yet lower-quality, priority-based
planner. At the same time, there has been a recent push for Multi-Agent
Reinforcement Learning (MARL) based MAPF algorithms, which let agents learn
decentralized policies that exhibit improved cooperation over such priority
planning, although inevitably remaining slower. In this paper, we introduce a
new MAPF algorithm, LNS2+RL, which combines the distinct yet complementary
characteristics of LNS2 and MARL to effectively balance their individual
limitations and get the best from both worlds. During early iterations, LNS2+RL
relies on MARL for low-level re-planning, which we show eliminates collisions
much more than a priority-based planner. There, our MARL-based planner allows
agents to reason about past and future/predicted information to gradually learn
cooperative decision-making through a finely designed curriculum learning. At
later stages of planning, LNS2+RL adaptively switches to priority-based
planning to quickly resolve the remaining collisions, naturally trading-off
solution quality and computational efficiency. Our comprehensive experiments on
challenging tasks across various team sizes, world sizes, and map structures
consistently demonstrate the superior performance of LNS2+RL compared to many
MAPF algorithms, including LNS2, LaCAM, and EECBS, where LNS2+RL shows
significantly better performance in complex scenarios. We finally
experimentally validate our algorithm in a hybrid simulation of a warehouse
mockup involving a team of 100 (real-world and simulated) robots.
