Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning

25 October 2021

Papers citing "Common Information based Approximate State Representations in Multi-Agent Reinforcement Learning"

5 / 5 papers shown

Title
Dual Filter: A Mathematical Framework for Inference using Transformer-like Architectures Heng-Sheng Chang P. Mehta 36 0 0 01 May 2025
A Novel Point-based Algorithm for Multi-agent Control Using the Common Information Approach Dengwang Tang A. Nayyar R. Jain 11 0 0 10 Apr 2023
Abstracting Imperfect Information Away from Two-Player Zero-Sum Games Samuel Sokota Ryan DÓrazio Chun Kai Ling David J. Wu J. Zico Kolter Noam Brown 24 4 0 22 Jan 2023
Approximate Information States for Worst-Case Control and Learning in Uncertain Systems Aditya Dave N. Venkatesh Andreas A. Malikopoulos 27 7 0 12 Jan 2023
Solving Common-Payoff Games with Approximate Policy Iteration Samuel Sokota Edward Lockhart Finbarr Timbers Elnaz Davoodi Ryan DÓrazio Neil Burch Martin Schmid Michael H. Bowling Marc Lanctot 42 22 0 11 Jan 2021