Review for NeurIPS paper: Independent Policy Gradient Methods for Competitive Reinforcement Learning

Weaknesses: I am not convinced by the main motivation of this paper for decoupled or independent learning. Specifically, from the communication perspective, once agents can also communicate the actions each other took per round, then each agent can also simulate any coupled algorithm locally (or only coupled online algorithm if has storage limitation). Since agents have to communicate with the oracle or environment in each round anyway, I don't see in practice why communicate the actions in the learning process is that problematic. Second, this paper says that the independent learning is important because it allows the algorithm "being versatile, being applicable even in uncertain environments where the type of interaction and number of other agents are not known to the agent. " I feel this description does not fit the algorithm studied in this paper, thus a bit misleading.