Before revision:  We present extensive results of PRIMAL2 in both MAPF and LMAPF environments with up to 1024 agents and compare its performance to complete state-of-the-art planners . We experimentally observe that agents successfully learn to follow ideal conventions and can exhibit selfless coordinated maneuvers that maximize joint rewards. We find that not only does PRIMAL2 significantly surpass our previous work , it is also able to perform on par and even outperform state-of-the-art planners in terms of throughput .
After revision:  We present extensive results of PRIMAL2 in both MAPF and LMAPF environments with up to 1024 agents and compare its performance to complete state-of-the-art planners in terms of makespan and throughput. We show that PRIMAL2 significantly surpass our previous work , it is also able to perform on par and even outperform state-of-the-art planners in terms of throughput .