  In this paper we address the problem of selecting factor-graph permutations
of polar codes under belief propagation (BP) decoding to significantly improve
the error-correction performance of the code. In particular, we formalize the
factor-graph permutation selection as the multi-armed bandit problem in
reinforcement learning and propose a decoder that acts like an online-learning
agent that learns to select the good factor-graph permutations during the
course of decoding. We use state-of-the-art algorithms for the multi-armed
bandit problem and show that for a 5G polar codes of length 128 with 64
information bits, the proposed decoder has an error-correction performance gain
of around 0.125 dB at the target frame error rate of 10^{-4}, when compared to
the approach that randomly selects the factor-graph permutations.
