2023
JMLR
JMLR 2023
Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games
Abstract
We consider a general-sum N-player linear-quadratic game with stochastic dynamics over a finite horizon and prove the global convergence of the natural policy gradient method to the Nash equilibrium. In order to prove convergence of the method we require a certain amount of noise in the system. We give a condition, essentially a lower bound on the covariance of the noise in terms of the model parameters, in order to guarantee convergence. We illustrate our results with numerical experiments to show that even in situations where the policy gradient method may not converge in the deterministic setting, the addition of noise leads to convergence. [abs] [ pdf ][ bib ] © JMLR 2023. (edit, beta)
🌉
Interdisciplinary Bridge
— Artificial Intelligence and Machine Learning and Mathematics & Optimization and Reinforcement Learning
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio