Graphon mean-field control for cooperative multi-agent reinforcement learning
From MaRDI portal
Publication:6136535
DOI10.1016/j.jfranklin.2023.09.002zbMath1530.93012arXiv2209.04808OpenAlexW4386511453MaRDI QIDQ6136535
Xiaoli Wei, Yuanquan Hu, Junji Yan, Hengxi Zhang
Publication date: 17 January 2024
Published in: Journal of the Franklin Institute (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2209.04808
Applications of graph theory (05C90) Learning and adaptive systems in artificial intelligence (68T05) Multi-agent systems (93A16)
Cites Work
- Unnamed Item
- Distributed learning and cooperative control for multi-agent systems
- Mean field games
- Mean field game of controls and an application to trade crowding
- Finite mean field games: fictitious play and convergence to a first order continuous mean field game
- Large population stochastic dynamic games: closed-loop McKean-Vlasov systems and the Nash certainty equivalence principle
- Probabilistic Analysis of Mean-Field Games
- Convergence of Weighted Empirical Measures
- Mean field games: A toy model on an Erdös-Renyi graph.
- High-Dimensional Statistics
- Mean-Field Controls with Q-Learning for Cooperative MARL: Convergence and Complexity Analysis
- Stochastic Graphon Games: I. The Static Case
- Graphon Control of Large-Scale Networks of Linear Systems
This page was built for publication: Graphon mean-field control for cooperative multi-agent reinforcement learning