Computational Approaches for Stochastic Shortest Path on Succinct MDPs
Computational Approaches for Stochastic Shortest Path on Succinct MDPs
Krishnendu Chatterjee, Hongfei Fu, Amir Goharshady, Nastaran Okati
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Main track. Pages 4700-4707.
https://doi.org/10.24963/ijcai.2018/653
We consider the stochastic shortest path (SSP) problem for succinct Markov decision processes (MDPs), where the MDP consists of a set of variables, and a set of nondeterministic rules that update the variables. First, we show that several examples from the AI literature can be modeled as succinct MDPs. Then we present computational approaches for upper and lower bounds for the SSP problem: (a) for computing upper bounds, our method is polynomial-time in the implicit description of the MDP; (b) for lower bounds, we present a polynomial-time (in the size of the implicit description) reduction to quadratic programming. Our approach is applicable even to infinite-state MDPs. Finally, we present experimental results to demonstrate the effectiveness of our approach on several classical examples from the AI literature.
Keywords:
Planning and Scheduling: Markov Decisions Processes
Planning and Scheduling: Theoretical Foundations of Planning
Planning and Scheduling: Planning under Uncertainty