Review for NeurIPS paper: Robust Multi-Agent Reinforcement Learning with Model Uncertainty

Weaknesses: - The biggest weakness of this paper in my mind is the clarity and framing. The paper motivates the contribution by stating that agents may not have access to the reward functions / models of other agents. For example, the paper states: "In many practical applications, the agents may not have perfect information of the model, i.e., the reward function and/or the transition probability model. For example, in an urban traffic network that involves multiple self-driving cars, each vehicle makes an individual action and has no access to other cars' rewards and models." However, most MARL methods don't make any assumptions about the reward function of other agents, particularly in the decentralized MARL setting.