A policy gradient method for semi-Markov decision processes with application to call admission control

Solving a semi-Markov decision process (SMDP) using value or policy iteration requires precise knowledge of the probabilistic model and suffers from the curse of dimensionality. To overcome these limitations, we present a reinforcement learning approach where one optimizes the SMDP performance criterion with respect to a family of parameterised policies. We propose an online algorithm that simultaneously estimates the gradient of the performance criterion and optimises it using stochastic approximation.