63 citations · 97 across the 21 of their papers we have counts for
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math.OC2017★ 4 cited
An Empirical Dynamic Programming Algorithm for Continuous MDPs
William B. Haskell, Rahul Jain, Hiteshi Sharma +1
We propose universal randomized function approximation-based empirical value iteration (EVI) algorithms for Markov decision processes. The `empirical' nature comes from each iterat…
math.OC2016
Random constraint sampling and duality for convex optimization
William B. Haskell, Yu Pengqian
We are interested in solving convex optimization problems with large numbers of constraints. Randomized algorithms, such as random constraint sampling, have been very successful in…
math.OC2015★ 2 cited
Central-limit approach to risk-aware Markov decision processes
Pengqian Yu, Jia Yuan Yu, Huan Xu
Whereas classical Markov decision processes maximize the expected reward, we consider minimizing the risk. We propose to evaluate the risk associated to a given policy over a long-…