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