Approximate Relative Value Learning for Average-reward Continuous State MDPs

@InProceedings{pmlr-v115-sharma20a,
  title = 	 {Approximate Relative Value Learning for Average-reward Continuous State MDPs},
  author =       {Sharma, Hiteshi and Jafarnia-Jahromi, Mehdi and Jain, Rahul},
  booktitle = 	 {Proceedings of The 35th Uncertainty in Artificial Intelligence Conference},
  pages = 	 {956--964},
  year = 	 {2020},
  editor = 	 {Adams, Ryan P. and Gogate, Vibhav},
  volume = 	 {115},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {22--25 Jul},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v115/sharma20a/sharma20a.pdf},
  url = 	 {https://proceedings.mlr.press/v115/sharma20a.html},
  abstract = 	 {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.}
}