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In this paper, we propose a reinforcement learning based algorithm forrate-profile construction of Arikan's Polarization Assisted Convolutional (PAC)codes. This method can be used for any blocklength, rate, list size undersuccessive cancellation list (SCL) decoding and convolutional precodingpolynomial. To the best of our knowledge, we present, for the first time, a setof new reward and update strategies which help the reinforcement learning agentdiscover much better rate-profiles than those present in existing literature.Simulation results show that PAC codes constructed with the proposed algorithmperform better in terms of frame erasure rate (FER) compared to the PAC codesconstructed with contemporary rate profiling designs for various list lengths.Further, by using a (64, 32) PAC code as an example, it is shown that thechoice of convolutional precoding polynomial can have a significant impact onrate-profile construction of PAC codes.

A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes