  Actors and critics in actor-critic reinforcement learning algorithms are
functionally separate, yet they often use the same network architectures. This
case study explores the performance impact of network sizes when considering
actor and critic architectures independently. By relaxing the assumption of
architectural symmetry, it is often possible for smaller actors to achieve
comparable policy performance to their symmetric counterparts. Our experiments
show up to 99% reduction in the number of network weights with an average
reduction of 77% over multiple actor-critic algorithms on 9 independent tasks.
Given that reducing actor complexity results in a direct reduction of run-time
inference cost, we believe configurations of actors and critics are aspects of
actor-critic design that deserve to be considered independently, particularly
in resource-constrained applications or when deploying multiple actors
simultaneously.
