Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs

Robbel, Philipp, Oliehoek, Frans A ORCID: 0000-0003-4372-5055 and Kochenderfer, Mykel J (2016) Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs. .

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Abstract

<jats:p> Many solution methods for Markov Decision Processes (MDPs) exploit structure in the problem and are based on value function factorization. Especially multiagent settings, however, are known to suffer from an exponential increase in value component sizes as interactions become denser, restricting problem sizes and types that can be handled. 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. We show how representational benefits from anonymity translate into computational efficiencies, both for variable elimination in a factor graph and for the approximate linear programming solution to factored MDPs. Our methods scale to factored MDPs that were previously unsolvable, such as the control of a stochastic disease process over densely connected graphs with 50 nodes and 25 agents. </jats:p>

Item Type:	Conference or Workshop Item (Unspecified)
Additional Information:	(To appear) bib2html_rescat: Multiagent Systems - (Approximate) Planning under Uncertainty bib2html_pubtype: Refereed Workshop
Depositing User:	Symplectic Admin
Date Deposited:	12 Apr 2016 11:33
Last Modified:	24 Mar 2024 19:37
DOI:	10.1609/aaai.v30i1.10133
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3000450