# Decentralized stochastic control

arXiv: https://arxiv.org/abs/1310.4545 · HTML: https://mlsanity.com/paper/1310.4545/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2013.

## Built on by (most cited first, 7 of 7)

- [A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.03859/) (2022)
- [Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2110.12603/) (2021)
- [MARL with General Utilities via Decentralized Shadow Reward Actor-Critic](https://mlsanity.com/paper/2106.00543/) (2021)
- [Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming](https://mlsanity.com/paper/2110.12929/) (2021)
- [Policy Evaluation in Decentralized POMDPs with Belief Sharing](https://mlsanity.com/paper/2302.04151/) (2023)
- [Hierarchical Decentralized Stochastic Control for Cyber-Physical Systems](https://mlsanity.com/paper/2506.22971/) (2025)
- [Provably Optimal Learning Algorithms for Assistance Games](https://mlsanity.com/paper/2607.08012/) (2026)

## 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.