Planning Under Uncertainty Using Reduced Models: Revisiting Determinization


  • Luis Pineda University of Massachusetts Amherst
  • Shlomo Zilberstein University of Massachusetts Amherst



Markov Decision Processes, Determinization, Continual Planning, Planning Under Uncertainty


We introduce a family of MDP reduced models characterized by two parameters: the maximum number of primary outcomes per action that are fully accounted for and the maximum number of occurrences of the remaining exceptional outcomes that are planned for in advance. Reduced models can be solved much faster using heuristic search algorithms such as LAO*, benefiting from the dramatic reduction in the number of reachable states. A commonly used determinization approach is a special case of this family of reductions, with one primary outcome per action and zero exceptional outcomes per plan. We present a framework to compute the benefits of planning with reduced models, relying on online planning when the number of exceptional outcomes exceeds the bound. Using this framework, we compare the performance of various reduced models and consider the challenge of generating good ones automatically. We show that each one of the dimensions---allowing more than one primary outcome or planning for some limited number of exceptions---could improve performance relative to standard determinization. The results place recent work on determinization in a broader context and lay the foundation for efficient and systematic exploration of the space of MDP model reductions.




How to Cite

Pineda, L., & Zilberstein, S. (2014). Planning Under Uncertainty Using Reduced Models: Revisiting Determinization. Proceedings of the International Conference on Automated Planning and Scheduling, 24(1), 217-225.