  We show in this work that reinforcement learning can be successfully applied
to decoding short to moderate length sparse graph-based channel codes.
Specifically, we focus on low-density parity check (LDPC) codes, which for
example have been standardized in the context of 5G cellular communication
systems due to their excellent error correcting performance. These codes are
typically decoded via belief propagation iterative decoding on the
corresponding bipartite (Tanner) graph of the code via flooding, i.e., all
check and variable nodes in the Tanner graph are updated at once. In contrast,
in this paper we utilize a sequential update policy which selects the optimum
check node (CN) scheduling in order to improve decoding performance. In
particular, we model the CN update process as a multi-armed bandit process with
dependent arms and employ a Q-learning scheme for optimizing the CN scheduling
policy. In order to reduce the learning complexity, we propose a novel
graph-induced CN clustering approach to partition the state space in such a way
that dependencies between clusters are minimized. Our results show that
compared to other decoding approaches from the literature, the proposed
reinforcement learning scheme not only significantly improves the decoding
performance, but also reduces the decoding complexity dramatically once the
scheduling policy is learned.
