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Found 2 papers, 1 papers with code
Algorithmic Analysis of Qualitative and Quantitative Termination Problems for Affine Probabilistic Programs
Firstly, we show that the membership problem of LRAPP (i) can be decided in polynomial time for APP's with at most demonic non-determinism, and (ii) is NP-hard and in PSPACE for APP's with angelic non-determinism; moreover, the NP-hardness result holds already for APP's without probability and demonic non-determinism.
Logic in Computer Science Programming Languages D.2.4
Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes
The objective of risk-constrainedplanning is to maximize the expected discounted-sum payoffamong risk-averse policies that ensure the probability to en-counter a failure state is below a desired threshold.
