# Continuous-Time Distributed Dynamic Programming for Networked Multi-Agent Markov Decision Processes

arXiv: https://arxiv.org/abs/2307.16706 · HTML: https://mlsanity.com/paper/2307.16706/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization](https://mlsanity.com/paper/1404.6264/) (2014)
- [Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization](https://mlsanity.com/paper/1806.00877/) (2018)
- [Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents](https://mlsanity.com/paper/1912.00498/) (2019)
- [Distributed Policy Evaluation Under Multiple Behavior Strategies](https://mlsanity.com/paper/1312.7606/) (2013)
- [A Discrete-Time Switching System Analysis of Q-learning](https://mlsanity.com/paper/2102.08583/) (2021)
- [Multi-Agent Fully Decentralized Value Function Learning with Linear Convergence Rates](https://mlsanity.com/paper/1810.07792/) (2018)
- [Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks](https://mlsanity.com/paper/2003.00433/) (2020)
- [Fast Multi-Agent Temporal-Difference Learning via Homotopy Stochastic Primal-Dual Optimization](https://mlsanity.com/paper/1908.02805/) (2019)

## Abstract (arXiv)

The main goal of this paper is to investigate continuous-time distributed dynamic programming (DP) algorithms for networked multi-agent Markov decision problems (MAMDPs). In our study, we adopt a distributed multi-agent framework where individual agents have access only to their own rewards, lacking insights into the rewards of other agents. Moreover, each agent has the ability to share its parameters with neighboring agents through a communication network, represented by a graph. We first introduce a novel distributed DP, inspired by the distributed optimization method of Wang and Elia. Next, a new distributed DP is introduced through a decoupling process. The convergence of the DP algorithms is proved through systems and control perspectives. The study in this paper sets the stage for new distributed temporal different learning algorithms.