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Creators/Authors contains: "Jafarnia-Jahromi, Mehdi"

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  1. ; ; (, IEEE)
  2. (, Proceedings of The 25th International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  3. ; (, Automatica)
    Full Text Available 
  4. ; ; ; ; (, International Conference on Machine Learning)
    null (Ed.)
    Accepted Manuscript Full Text Available
  5. ; ; (, Proceedings UAI)
    In this paper, we propose an approximate rela- tive 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 approx- imation via nearest neighbors. The theoreti- cal 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 eval- uate the proposed algorithm on a benchmark problem numerically. 
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    Accepted Manuscript Full Text Available