Central-limit approach to risk-aware Markov decision processes
arXiv:1512.00583
Abstract
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-enough time horizon with the help of a central limit theorem. The proposed approach works whether the transition probabilities are known or not. We also provide a gradient-based policy improvement algorithm that converges to a local optimum of the risk objective.
arXiv admin note: text overlap with arXiv:1403.6530 by other authors