Relaxation for constrained decentralized Markov decision processes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper studies a class of decentralized multi-agent stochastic optimization problems. In these problems, each agent has only a partial view of the world state, and a partial control of the actions but must cooperatively maximize the long-term system reward. The state that an agent observe consists of two parts - a common public component and an agent-specific private component. Importantly, taking actions incurs costs and the actions that the agents can take are subject to an overall cost constraint in each interaction period. We formulate this problem as an infinite time horizon Decentralized Markov Decision Process (DEC-MDP) with resource constraints and develop efficient approximate algorithms that allow decentralized computation of the agent policy based on Lagrangian relaxation.

Original languageEnglish (US)
Title of host publicationAAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages1313-1314
Number of pages2
ISBN (Electronic)9781450342391
StatePublished - 2016
Event15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016 - Singapore, Singapore
Duration: May 9 2016May 13 2016

Other

Other15th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2016
CountrySingapore
CitySingapore
Period5/9/165/13/16

Keywords

  • Decentralized MDP
  • Lagrangian relaxation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering

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  • Cite this

    Xu, J. (2016). Relaxation for constrained decentralized Markov decision processes. In AAMAS 2016 - Proceedings of the 2016 International Conference on Autonomous Agents and Multiagent Systems (pp. 1313-1314). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).