Decentralized stochastic control
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Abstract (arXiv)
Decentralized stochastic control refers to the multi-stage optimization of a dynamical system by multiple controllers that have access to different information. Decentralization of information gives rise to new conceptual challenges that require new solution approaches. In this expository paper, we use the notion of an information-state to explain the two commonly used solution approaches to decentralized control: the person-by-person approach and the common-information approach.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Oct 2013.
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Citing papers per year: 2021: 3; 2022: 1; 2023: 1; 2024: 0; 2025: 1; 2026: 1.
7 papers.
- 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learningcited by 12
- 2021Common Information based Approximate State Representations in Multi-Agent Reinforcement Learningcited by 8
- 2021MARL with General Utilities via Decentralized Shadow Reward Actor-Criticcited by 5
- 2021Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programmingcited by 1
- 2023Policy Evaluation in Decentralized POMDPs with Belief Sharingcited by 1
- 2025Hierarchical Decentralized Stochastic Control for Cyber-Physical Systemscited by 0
- 2026Provably Optimal Learning Algorithms for Assistance Gamescited by 0
- 2026Provably Optimal Learning Algorithms for Assistance Games9 Jul 2026
- 2025Hierarchical Decentralized Stochastic Control for Cyber-Physical Systems28 Jun 2025
- 2023Policy Evaluation in Decentralized POMDPs with Belief Sharing8 Feb 2023
- 2022A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning8 Sep 2022
- 2021Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning25 Oct 2021
- 2021Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming22 Oct 2021
- 2021MARL with General Utilities via Decentralized Shadow Reward Actor-Critic29 May 2021