  This paper presents an enhanced belief propagation (BP) decoding algorithm
and a reinforcement learning-based BP decoding algorithm for polar codes. The
enhanced BP algorithm weighs each Processing Element (PE) input based on their
signals and Euclidean distances using a heuristic metric. The proposed
reinforcement learning-based BP decoding strategy relies on reweighting the
messages and consists of two steps: we first weight each PE input based on
their signals and Euclidean distances using a heuristic metric, then a
Q-learning algorithm (QLBP) is employed to figure out the best correction
factor for successful decoding. Simulations show that the proposed enhanced BP
and QLBP decoders outperform the successive cancellation (SC) and belief
propagation (BP) decoders, and approach the SCL decoders.
