Paper Info
Title
Of Cores: A Partial-Exploration Framework For Markov Decision Processes
Abstract
We introduce a framework for approximate analysis of Markov decision processes (MDP) with bounded-, unbounded-, and infinite-horizon properties. The main idea is to identify a core of an MDP, i.e., a subsystem where we provably remain with high probability, and to avoid computation on the less relevant rest of the state space. Although we identify the core using simulations and statistical techniques, it allows for rigorous error bounds in the analysis. We obtain efficient analysis algorithms based on partial exploration for various settings, including the challenging case of strongly connected systems.
Year
DOI
Venue
2020
10.23638/LMCS-16(4:3)2020
LOGICAL METHODS IN COMPUTER SCIENCE
DocType
Volume
Issue
Journal
16
4
ISSN
Citations 
PageRank 
1860-5974
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Jan Kretínský115916.02
Tobias Meggendorfer2153.90