Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach

18 May 2021

Papers citing "Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach"

7 / 7 papers shown

Title
Symmetry-Guided Multi-Agent Inverse Reinforcement Learning Yongkai Tian Yirong Qi Xin Yu Wenjun Wu Jie Luo 123 1 0 10 Sep 2025
Solving Multi-Entity Robotic Problems Using Permutation Invariant Neural Networks Tianxu An Joonho Lee Marko Bjelonic Flavio De Vincenti Marco Hutter 184 4 0 28 Feb 2024
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARLNeural Information Processing Systems (NeurIPS), 2022 Fengzhuo Zhang Boyi Liu Kaixin Wang Vincent Y. F. Tan Zhuoran Yang Zhaoran Wang OffRL LRM 188 13 0 20 Sep 2022
Graphon Mean-Field Control for Cooperative Multi-Agent Reinforcement LearningJournal of the Franklin Institute (JFI), 2022 Yuanquan Hu Xiaoli Wei Jun Yan Heng-Wei Zhang 159 12 0 11 Sep 2022
Learning to Coordinate in Multi-Agent Systems: A Coordinated Actor-Critic Algorithm and Finite-Time GuaranteesConference on Learning for Dynamics & Control (L4DC), 2021 Siliang Zeng Tianyi Chen Alfredo García Mingyi Hong 198 15 0 11 Oct 2021
WaveCorr: Correlation-savvy Deep Reinforcement Learning for Portfolio Management S. Marzban Erick Delage Jonathan Yu-Meng Li J. Desgagne-Bouchard C. Dussault 174 1 0 14 Sep 2021
Efficient Model-Based Multi-Agent Mean-Field Reinforcement Learning Barna Pásztor Ilija Bogunovic Andreas Krause 247 49 0 08 Jul 2021