Reviews: Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control

The paper proposes Variance Based Control (VBC) of communications in cooperative multi-agent RL settings. As noted in the Abstract, VBC achieved 2x-10x reduction in communication overhead compared to state-of-the-art MARL settings. The paper also gives a proof of convergence in a tabular setting. In the initial reviews, R4 gave strongest support with a score of 9, while R1 and R2 gave positive overall scores but only at marginally above threshold (6). After receiving the author feedback, there were minimal updates to the original reviews, e.g., R2 said "After going over the author response I appreciate the extra analysis put into comparing the method to MADDPG to make sure it is state of the art. It is good to compare these methods across previous benchmarks to show improvement. While the additional hyperparameter analysis is helpful it is a bit obvious of what is normally done. Some discussion on the effects of specific settings might shed more light on how the method works."