  Reinforcement learning is important part of artificial intelligence. In this
paper, we review model-free reinforcement learning that utilizes the average
reward optimality criterion in the infinite horizon setting. Motivated by the
solo survey by Mahadevan (1996a), we provide an updated review of work in this
area and extend it to cover policy-iteration and function approximation methods
(in addition to the value-iteration and tabular counterparts). We present a
comprehensive literature mapping. We also identify and discuss opportunities
for future work.
