Review for NeurIPS paper: Reinforcement Learning with Feedback Graphs

Additional Feedback: This paper addresses the problem of an RL agent that receives additional observations, after executing every action, which provide it with information about possible transitions that it could have experienced. These side observations might be generated, for instance, by auxiliary sensors. The authors formalize this setting by can defining a feedback graph based on the additional observations. Feedback graphs may be used by model-based RL algorithms to learn more efficiently. In particular, the authors show that the regret of the resulting model-based algorithm is bounded by certain properties of the graph, instead of depending on the number of states and actions that exist in the original problem (without side observations).