In recent years, Multi-Agent Deep Reinforcement Learning (MADRL) has been successfully applied to various complex scenarios such as playing computer games and coordinating robot swarms. In this talk, we investigate the impact of “implementation tricks” for SOTA cooperative MADRL algorithms, such as QMIX, and provide some suggestions for tuning. In investigating implementation settings and how they affect fairness in MADRL experiments, we found some conclusions contrary to the previous work; we discuss how QMIX’s monotonicity condition is critical for cooperative tasks. Finally, we propose the new policy-based algorithm RIIT that achieves SOTA among policy-based algorithms.

https://arxiv.org/pdf/2102.03479.pdf