2 citations · 3 across the 5 of their papers we have counts for
6 papers
EEG-Based Brain-LLM Interface for Human Preference Aligned Generation
Junzi Zhang, Jianing Shen, Weijie Tu +5
Large language models (LLMs) are becoming an increasingly important component of human--computer interaction, enabling users to coordinate a wide range of intelligent agents throug…
MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games
Anran Hu, Junzi Zhang
Reinforcement learning for multi-agent games has attracted lots of attention recently. However, given the challenge of solving Nash equilibria for large population games, existing…
MESOB: Balancing Equilibria & Social Optimality
Xin Guo, Lihong Li, Sareh Nabi +2
Motivated by bid recommendation in online ad auctions, this paper considers a general class of multi-level and multi-agent games, with two major characteristics: one is a large num…
MFGLib: A Library for Mean-Field Games
Xin Guo, Anran Hu, Matteo Santamaria +2
Mean-field games (MFGs) are limiting models to approximate -player games, with a number of applications. Despite the ever-growing numerical literature on computation of MFGs, th…
MF-OMO: An Optimization Formulation of Mean-Field Games
Xin Guo, Anran Hu, Junzi Zhang
This paper proposes a new mathematical paradigm to analyze discrete-time mean-field games. It is shown that finding Nash equilibrium solutions for a general class of discrete-time…
Theoretical Guarantees of Fictitious Discount Algorithms for Episodic Reinforcement Learning and Global Convergence of Policy Gradient Methods
Xin Guo, Anran Hu, Junzi Zhang
When designing algorithms for finite-time-horizon episodic reinforcement learning problems, a common approach is to introduce a fictitious discount factor and use stationary polici…