Exploiting Anonymity in Approximate Linear Programming: Scaling to Large
Multiagent MDPs (Extended Version)

v1v2 (latest)

Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version)

29 November 2015

Mykel J. Kochenderfer

ArXiv (abs)PDF HTML

Papers citing "Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version)"

5 / 5 papers shown

Title
A Survey on Large-Population Systems and Scalable Multi-Agent Reinforcement Learning Kai Cui Anam Tahir Gizem Ekinci Ahmed Elshamanhory Yannick Eich Mengguang Li Heinz Koeppl AI4CE 164 15 0 08 Sep 2022
Multi-Agent MDP Homomorphic Networks Elise van der Pol H. V. Hoof F. Oliehoek Max Welling AI4CE 93 30 0 09 Oct 2021
Exchangeable Input Representations for Reinforcement Learning John Mern Dorsa Sadigh Mykel J. Kochenderfer 98 4 0 19 Mar 2020
A Sufficient Statistic for Influence in Structured Multiagent Environments F. Oliehoek Stefan J. Witwicki L. Kaelbling 81 23 0 22 Jul 2019
Policy Gradient With Value Function Approximation For Collective Multiagent Planning D. Nguyen Akshat Kumar H. Lau 96 43 0 09 Apr 2018