no code implementations • 1 May 2024 • Anran Hu, Junzi Zhang
MF-OML is the first fully polynomial multi-agent reinforcement learning algorithm for provably solving Nash equilibria (up to mean-field approximation gaps that vanish as the number of players $N$ goes to infinity) beyond variants of zero-sum and potential games.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 13 Sep 2021 • Xin Guo, Anran Hu, Junzi Zhang
To our best knowledge, this is the first theoretical guarantee on fictitious discount algorithms for the episodic reinforcement learning of finite-time-horizon MDPs, which also leads to the (first) global convergence of policy gradient methods for finite-time-horizon episodic reinforcement learning.
no code implementations • 19 Apr 2021 • Xin Guo, Anran Hu, Yufei Zhang
We study finite-time horizon continuous-time linear-convex reinforcement learning problems in an episodic setting.
no code implementations • 27 Jun 2020 • Matteo Basei, Xin Guo, Anran Hu, Yufei Zhang
We study finite-time horizon continuous-time linear-quadratic reinforcement learning problems in an episodic setting, where both the state and control coefficients are unknown to the controller.
no code implementations • 13 Mar 2020 • Xin Guo, Anran Hu, Renyuan Xu, Junzi Zhang
This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population.