  Markov decision processes (MDPs) are the defacto frame-work for sequential
decision making in the presence ofstochastic uncertainty. A classical
optimization criterion forMDPs is to maximize the expected discounted-sum
pay-off, which ignores low probability catastrophic events withhighly negative
impact on the system. On the other hand,risk-averse policies require the
probability of undesirableevents to be below a given threshold, but they do not
accountfor optimization of the expected payoff. We consider MDPswith
discounted-sum payoff with failure states which repre-sent catastrophic
outcomes. 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. Our maincontribution
is an efficient risk-constrained planning algo-rithm that combines UCT-like
search with a predictor learnedthrough interaction with the MDP (in the style
of AlphaZero)and with a risk-constrained action selection via linear
pro-gramming. We demonstrate the effectiveness of our approachwith experiments
on classical MDPs from the literature, in-cluding benchmarks with an order of
10^6 states.
