Search Results for author: Anran Hu

Found 5 papers, 0 papers with code

MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games

no code implementations1 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

Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods

no code implementations13 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.

Policy Gradient Methods reinforcement-learning +2

Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls

no code implementations19 Apr 2021 Xin Guo, Anran Hu, Yufei Zhang

We study finite-time horizon continuous-time linear-convex reinforcement learning problems in an episodic setting.

Reinforcement Learning (RL)

Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon

no code implementations27 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.

Reinforcement Learning (RL)

A General Framework for Learning Mean-Field Games

no code implementations13 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.

Decision Making Multi-agent Reinforcement Learning +4

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