倉庫ロボットの協調制御におけるマルチエージェント強化学習アルゴリズムの比較研究
MARL Warehouse Robots
倉庫ロボットの協調作業に対してQMIXとIPPOを比較し、QMIXが優れた性能を示す一方でハイパーパラメータ調整が必要であることを明らかにした。
著者: Price Allman, Lian Thang, Dre Simmons, Salmon Riaz
分類: cs.AI, cs.RO
原文アブストラクト
We present a comparative study of multi-agent reinforcement learning (MARL) algorithms for cooperative warehouse robotics. We evaluate QMIX and IPPO on the Robotic Warehouse (RWARE) environment and a custom Unity 3D simulation. Our experiments reveal that QMIX's value decomposition significantly outperforms independent learning approaches (achieving 3.25 mean return vs. 0.38 for advanced IPPO), but requires extensive hyperparameter tuning -- particularly extended epsilon annealing (5M+ steps) for sparse reward discovery. We demonstrate successful deployment in Unity ML-Agents, achieving consistent package delivery after 1M training steps. While MARL shows promise for small-scale deployments (2-4 robots), significant scaling challenges remain. Code and analyses: https://pallman14.github.io/MARL-QMIX-Warehouse-Robots/