Review for NeurIPS paper: Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes

This paper proposes application of reinforcement learning, in particular Q-learning, to determine the check-node (CN) scheduling policy in BP decoding of short LDPC codes. It is in contrast to other works in the broad area of machine learning applications to coding which focus on finding coding schemes or "deep unfolding" of iterative decoders. Discretization of state space and clustering of CNs are introduced to avoid explosion of the state space size and learning complexity. The reviewers rated this paper favorably, especially with emphasis on the novelty. They are also satisfied with the author response.