IntervalMDP.jl

Accelerated Value Iteration for Interval Markov Decision Processes

Journal article (2024)

Authors

Frederik Baymler Mathiesen Team Luca Laurenti - Mechanical, Maritime and Materials Engineering
Morteza Lahijanian University of Colorado
L. Laurenti Team Luca Laurenti - Mechanical, Maritime and Materials Engineering
Research Group
Team Luca Laurenti (Mechanical, Maritime and Materials Engineering) (TU Delft)
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Published Date

2024

Language

English

Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Luca Laurenti

Abstract

In this paper, we present IntervalMDP.jl, a Julia package for probabilistic analysis of interval Markov Decision Processes (IMDPs). IntervalMDP.jl facilitates the synthesis of optimal strategies and verification of IMDPs against reachability specifications and discounted reward properties. The library supports sparse matrices and is compatible with data formats from common tools for the analysis of probabilistic models, such as PRISM. A key feature of IntervalMDP.jl is that it presents both a multi-threaded CPU and a GPU-accelerated implementation of value iteration algorithms for IMDPs. In particular, IntervalMDP.jl takes advantage of the Julia type system and the inherently parallelizable nature of value iteration to improve the efficiency of performing analysis of IMDPs. On a set of examples, we show that IntervalMDP.jl substantially outperforms existing tools for verification and strategy synthesis for IMDPs in both computation time and memory consumption.