# MICPO

The work identifies that in decentralized multi-agent path finding, even correctly learned per-agent action distributions can be recombined by independent sampling into incompatible joint actions, and proposes DMM, which replaces one-shot sampling with discrete iterative refinement of action intents across communication rounds before commitment, fine-tuned by MICPO, a critic-free multi-agent reinforcement-learning method; DMM generally achieves higher success rates and lower solution costs on POGEMA, solves 1,598 of 1,600 MovingAI tasks (the highest coverage among the evaluated methods), and scales to over one million simultaneously acting agents.

The work identifies that in decentralized multi-agent path finding, even correctly learned per-agent action distributions can be recombined by independent sampling into incompatible joint actions, and proposes DMM, which replaces one-shot sampling with discrete iterative refinement of action intents across communication rounds before commitment, fine-tuned by MICPO, a critic-free multi-agent reinforcement-learning method; DMM generally achieves higher success rates and lower solution costs on POGEMA, solves 1,598 of 1,600 MovingAI tasks (the highest coverage among the evaluated methods), and scales to over one million simultaneously acting agents.