Solving Uncertain MDPs with Objectives that are Separable over Instantiations of Model Uncertainty
Conference Proceeding Article
Markov Decision Problems, MDPs offer an effective mech- anism for planning under uncertainty. However, due to unavoidable uncertainty over models, it is difficult to obtain an exact specification of an MDP. We are interested in solving MDPs, where transition and reward functions are not exactly specified. Existing research has primarily focussed on computing infinite horizon stationary policies when optimizing robustness, regret and percentile based objectives. We focus specifically on finite horizon problems with a special emphasis on objectives that are separable over individual instantiations of model uncertainty (i.e., objectives that can be expressed as a sum over instantiations of model uncertainty): (a) First, we identify two separable objectives for uncertain MDPs: Average Value Maximization (AVM) and Confidence Probability Maximisation (CPM). (b) Second, we provide optimization based solutions to compute policies for uncertain MDPs with such objectives. In particular, we exploit the separability of AVM and CPM objectives by employing Lagrangian dual decomposition (LDD). (c) Finally, we demonstrate the utility of the LDD approach on a benchmark problem from the literature.
Markov Decision Problems (MDPs), Lagrangian Dual Decomposition; Bayesian Reinforcement Learning; Robust MDPs
Artificial Intelligence and Robotics | Computer Sciences | Numerical Analysis and Scientific Computing
Intelligent Systems and Decision Analytics
Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence: 25-30 January 2015, Austin, Texas USA
City or Country
Palo Alto, CA
ADULYASAK, Yossiri; VARAKANTHAM, Pradeep; AHMED, Asrar; and JAILLET, Patrick.
Solving Uncertain MDPs with Objectives that are Separable over Instantiations of Model Uncertainty. (2015). Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence: 25-30 January 2015, Austin, Texas USA. 3454-3460. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2916