Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Stochastic Shortest Path Games
SIAM Journal on Control and Optimization
Bounded-parameter Markov decision process
Artificial Intelligence
Controlled Markov set-chains under average criteria
Applied Mathematics and Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
An Empirical Evaluation of Interval Estimation for Markov Decision Processes
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Planning under risk and Knightian uncertainty
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficient solutions to factored MDPs with imprecise transition probabilities
Artificial Intelligence
International Journal of Approximate Reasoning
Solving uncertain markov decision problems: an interval-based method
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
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Stochastic Shortest Path problems (SSPs), a subclass of Markov Decision Problems (MDPs), can be efficiently dealt with using Real-Time Dynamic Programming (RTDP). Yet, MDP models are often uncertain (obtained through statistics or guessing). The usual approach is robust planning: searching for the best policy under the worst model. This paper shows how RTDP can be made robust in the common case where transition probabilities are known to lie in a given interval.