Conference Proceeding Article
Decentralized Markov Decision Process (Dec-MDP) providesa rich framework to represent cooperative decentralizedand stochastic planning problems under transition uncertainty.However, solving a Dec-MDP to generate coordinatedyet decentralized policies is NEXP-Hard. Researchershave made significant progress in providing approximate approachesto improve scalability with respect to number ofagents. However, there has been little or no research devotedto finding guarantees on solution quality for approximateapproaches considering multiple (more than 2 agents)agents. We have a similar situation with respect to the competitivedecentralized planning problem and the StochasticGame (SG) model. To address this, we identify models in thecooperative and competitive case that rely on submodular rewards,where we show that existing approximate approachescan provide strong quality guarantees (a priori, and for cooperativecase also posteriori guarantees). We then providesolution approaches and demonstrate improved online guaranteeson benchmark problems from the literature for the cooperativecase.
Databases and Information Systems | Software Engineering
Information Systems and Management
AAAI Conference on Artificial Intelligence (AAAI)
City or Country
San Fransisco, USA
KUMAR, Rajiv Ranjan; Pradeep VARAKANTHAM; and Akshat KUMAR.
Decentralized planning in stochastic environments with submodular rewards. (2017). AAAI Conference on Artificial Intelligence (AAAI). 3021-3028. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/3549
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