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Conditional Value-at-Risk for Reachability and Mean Payoff in Markov Decision Processes
[article]
2018
arXiv
pre-print
We present the conditional value-at-risk (CVaR) in the context of Markov chains and Markov decision processes with reachability and mean-payoff objectives. CVaR quantifies risk by means of the expectation of the worst p-quantile. As such it can be used to design risk-averse systems. We consider not only CVaR constraints, but also introduce their conjunction with expectation constraints and quantile constraints (value-at-risk, VaR). We derive lower and upper bounds on the computational
arXiv:1805.02946v1
fatcat:5p4vkm7hgzgw3cpsrnt25elfzu