Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version)



Robbel, Philipp, Oliehoek, Frans A ORCID: 0000-0003-4372-5055 and Kochenderfer, Mykel J
(2015) Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version). In: AAAI Fall Symposium on Sequential Decision Making in Intelligent Agents, Westin Arlington Gateway in Arlington, Virginia adjacent to Washington, DC.

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Abstract

Many exact and approximate solution methods for Markov Decision Processes (MDPs) attempt to exploit structure in the problem and are based on factorization of the value function. Especially multiagent settings, however, are known to suffer from an exponential increase in value component sizes as interactions become denser, meaning that approximation architectures are restricted in the problem sizes and types they can handle. We present an approach to mitigate this limitation for certain types of multiagent systems, exploiting a property that can be thought of as "anonymous influence" in the factored MDP. Anonymous influence summarizes joint variable effects efficiently whenever the explicit representation of variable identity in the problem can be avoided. We show how representational benefits from anonymity translate into computational efficiencies, both for general variable elimination in a factor graph but in particular also for the approximate linear programming solution to factored MDPs. The latter allows to scale linear programming to factored MDPs that were previously unsolvable. Our results are shown for the control of a stochastic disease process over a densely connected graph with 50 nodes and 25 agents.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Extended version of AAAI 2016 paper
Uncontrolled Keywords: cs.AI, cs.AI, cs.MA
Subjects: ?? QA75 ??
Depositing User: Symplectic Admin
Date Deposited: 26 Nov 2015 09:21
Last Modified: 17 Dec 2022 01:33
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/2039179

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