Discounted MDP's: distribution functions and exponential utility maximization
SIAM Journal on Control and Optimization
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Mathematics of Operations Research
Robust Control of Markov Decision Processes with Uncertain Transition Matrices
Operations Research
Robust, risk-sensitive, and data-driven control of markov decision processes
Robust, risk-sensitive, and data-driven control of markov decision processes
Risk-sensitive planning with one-switch utility functions: value iteration
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Percentile Optimization for Markov Decision Processes with Parameter Uncertainty
Operations Research
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The Markov decision process model is a powerful tool in planing tasks and sequential decision making problems. The randomness of state transitions and rewards implies that the performance of a policy is often stochastic. In contrast to the standard approach that studies the expected performance, we consider the policy that maximizes the probability of achieving a pre-determined target performance, a criterion we term probabilistic goal Markov decision processes. We show that this problem is NP-hard, but can be solved using a pseudo-polynomial algorithm. We further consider a variant dubbed "chance-constraint Markov decision problems," that treats the probability of achieving target performance as a constraint instead of the maximizing objective. This variant is NP-hard, but can be solved in pseudo-polynomial time.