Mathematics of Operations Research
Bounded-parameter Markov decision process
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Neuro-Dynamic Programming
L2-Gain and Passivity Techniques in Nonlinear Control
L2-Gain and Passivity Techniques in Nonlinear Control
Mathematics of Operations Research
Bias and Variance Approximation in Value Function Estimates
Management Science
Robust Control of Markov Decision Processes with Uncertain Transition Matrices
Operations Research
Convex Approximations of Chance Constrained Programs
SIAM Journal on Optimization
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Theory and Applications of Robust Optimization
SIAM Review
A Distributional Interpretation of Robust Optimization
Mathematics of Operations Research
Distributionally Robust Markov Decision Processes
Mathematics of Operations Research
Probabilistic goal Markov decision processes
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Optimization Under Probabilistic Envelope Constraints
Operations Research
A framework for computing bounds for the return of a policy
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Robust Markov Decision Processes
Mathematics of Operations Research
Shortest stochastic path with risk sensitive evaluation
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Robust Modified Policy Iteration
INFORMS Journal on Computing
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Markov decision processes are an effective tool in modeling decision making in uncertain dynamic environments. Because the parameters of these models typically are estimated from data or learned from experience, it is not surprising that the actual performance of a chosen strategy often differs significantly from the designer's initial expectations due to unavoidable modeling ambiguity. In this paper, we present a set of percentile criteria that are conceptually natural and representative of the trade-off between optimistic and pessimistic views of the question. We study the use of these criteria under different forms of uncertainty for both the rewards and the transitions. Some forms are shown to be efficiently solvable and others highly intractable. In each case, we outline solution concepts that take parametric uncertainty into account in the process of decision making.