Robust online optimization of reward-uncertain MDPs

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2011, Pages 2165-2171

  Regan, Kevin ^a   Boutilier, Craig ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ANY-TIME ALGORITHMS; APPROXIMATION SCHEME; COMPUTATIONAL TRACTABILITY; ERROR BOUND; MARKOV DECISION PROCESSES; MINIMAX REGRET; ONLINE OPTIMIZATION; REWARD FUNCTION;

ERROR ANALYSIS; MARKOV PROCESSES;

ARTIFICIAL INTELLIGENCE;

EID: 84881084517     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.5591/978-1-57735-516-8/IJCAI11-361     Document Type: Conference Paper

References (19)

1. David Avis. lrs: A revised implementation of the reverse search vertex enumeration algorithm. In Polytopes-Combinatorics and Computation, pages 177-198. Birkhauser-Verlag, 2000.
2. Andrew Bagnell, Andrew Ng, and Jeff Schneider. Solving uncertain Markov decision problems. Technical Report CMU-RI-TR-01-25, Carnegie Mellon University, Pittsburgh, 2003.
3. Ralphen Becker, Shlomo Zilberstein, Victor R. Lesser, and Claudia V. Goldman. Solving transition independent decentralized Markov decision processes. Journal of Artificial Intelligence Research, 22:423-455, 2004.
4. Jennifer Boger, Pascal Poupart, Jesse Hoey, Craig Boutilier, Geoff Fernie, and Alex Mihailidis. A planning system based on Markov decision processes to guide people with dementia through activities of daily living. IEEE Transactions on Information Technology in Biomedicine, 10(2):323-333, 2006.
5. Craig Boutilier, Relu Patrascu, Pascal Poupart, and Dale Schuurmans. Constraint-based optimization and utility elicitation using the minimax decision criterion. Artifical Intelligence, 170(8-9):686-713, 2006.
6. Hsien-Te Cheng. Algorithms for Partially Observable Markov Decision Processes. PhD thesis, University of British Columbia, Vancouver, 1988.
7. Erick Delage and Shie Mannor. Percentile optimization in uncertain Markov decision processes with application to efficient exploration. In Proceedings of the Twenty-fourth International Conference on Machine Learning (ICML-07), pages 225-232, Corvallis, OR, 2007.
8. Simon French. Decision Theory. Halsted, New York, 1986.
9. G. Iyengar. Robust dynamic programming. Mathematics of Operations Research, 30(2):1-21, 2005.
10. Brendan McMahan, Geoffrey Gordon, and Avrim Blum. Planning in the presence of cost functions controlled by an adversary. In Proceedings of the Twentieth International Conference on Machine Learning (ICML-03), pages 536-543, Washington, DC, 2003.
11. Andrew Ng and Stuart Russell. Algorithms for inverse reinforcement learning. In Proceedings of the Seventeenth International Conference on Machine Learning (ICML-00), pages 663-670, Stanford, CA, 2000.
12. Arnab Nilim and Laurent El Ghaoui. Robust control of Markov decision processes with uncertain transition matrices. Operations Research, 53(1):780-798, 2005.
13. Marek Petrik and Shlomo Zilberstein. A bilinear programming approach for multiagent planning. Journal of Artificial Intelligence Research, 35(1):235-274, 2009.
14. Martin L. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley, New York, 1994.
15. Kevin Regan and Craig Boutilier. Regret-based reward elicitation for Markov decision processes. In Proceedings of the Twenty-fifth Conference on Uncertainty in Artificial Intelligence (UAI-09), pages 454-451, Montreal, 2009.
16. Kevin Regan and Craig Boutilier. Robust policy computation in reward-uncertain MDPs using nondominated policies. In Proceedings of the Twenty-fourth AAAI Conference on Artificial Intelligence (AAAI-10), pages 1127-1133, Atlanta, 2010.
17. Kevin Regan and Craig Boutilier. Eliciting additive reward functions for Markov decision processes. In Proceedings of the Twenty-second International Joint Conference on Artificial Intelligence (IJCAI-11), Barcelona, 2009. To appear.
18. Leonard J. Savage. The Foundations of Statistics. Wiley, New York, 1954.
19. Huan Xu and Shie Mannor. Parametric regret in uncertain Markov decision processes. In 48th IEEE Conference on Decision and Control, pages 3606-3613, Shanghai, 2009.