Approximate Relative Value Learning for Average-reward Continuous State MDPs
Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, PMLR 115:956-964, 2020.
In this paper, we propose an approximate relative value learning (ARVL) algorithm for non- parametric MDPs with continuous state space and finite actions and average reward criterion. It is a sampling based algorithm combined with kernel density estimation and function approximation via nearest neighbors. The theoretical analysis is done via a random contraction operator framework and stochastic dominance argument. This is the first such algorithm for continuous state space MDPs with average re- ward criteria with these provable properties which does not require any discretization of state space as far as we know. We then evaluate the proposed algorithm on a benchmark problem numerically.