April 2, 2024, 7:42 p.m. | Muhammad Aneeq uz Zaman, Shubham Aggarwal, Melih Bastopcu, Tamer Ba\c{s}ar

cs.LG updates on arXiv.org arxiv.org

arXiv:2404.00045v1 Announce Type: cross
Abstract: In this paper, we investigate the impact of introducing relative entropy regularization on the Nash Equilibria (NE) of General-Sum $N$-agent games, revealing the fact that the NE of such games conform to linear Gaussian policies. Moreover, it delineates sufficient conditions, contingent upon the adequacy of entropy regularization, for the uniqueness of the NE within the game. As Policy Optimization serves as a foundational approach for Reinforcement Learning (RL) techniques aimed at finding the NE, in …

abstract agent arxiv cs.ai cs.gt cs.lg cs.ma entropy equilibria equilibrium games general impact linear nash equilibrium optimization paper policies policy regularization type

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Risk Management - Machine Learning and Model Delivery Services, Product Associate - Senior Associate-

@ JPMorgan Chase & Co. | Wilmington, DE, United States

Senior ML Engineer (Speech/ASR)

@ ObserveAI | Bengaluru