Consolidated actor-critic model for partially-observable Markov decision processes

A method for consolidating the traditionally separate actor and critic neural networks in temporal difference learning for addressing partially-observable Markov decision processes (POMDPs) is presented. Simulation results for solving a five-state POMDP problem support the claim that the consolidated model achieves higher performance while reducing computational and storage requirements to approximately half those of the traditional approach.