12 citations · 34 across the 14 of their papers we have counts for
17 papers
Decentralized Decision-Making among Heterogeneous Autonomous Vehicles: An -Potential Game Framework
Anran Hu, Zhexin Wang, Yufei Zhang +1
We study noncooperative multi-vehicle games among heterogeneous autonomous vehicles, where each vehicle adopts a decentralized closed-loop policy based on its own state, and optimi…
Dynamic Portfolio Optimization under CVaR Constraints
Anran Hu, Silvana M. Pesenti, Xiaofei Shi
We study continuous-time dynamic portfolio optimization under a Conditional Value-at-Risk (CVaR) constraint on the investor's terminal loss. For a general class of convex trading o…
-Potential Games for Decentralized Control of Connected and Automated Vehicles
Xuan Di, Anran Hu, Zhexin Wang +1
Designing scalable and safe control strategies for large populations of connected and automated vehicles (CAVs) requires accounting for strategic interactions among heterogeneous a…
Mean-Field Games with Constraints
Anran Hu, Zijiu Lyu
This paper introduces a framework of Constrained Mean-Field Games (CMFGs), where each agent solves a constrained Markov decision process (CMDP). This formulation captures scenarios…
Continuous-time mean field games: a primal-dual characterization
Xin Guo, Anran Hu, Jiacheng Zhang +1
This paper establishes a primal-dual formulation for continuous-time mean field games (MFGs) and provides a complete analytical characterization of the set of all Nash equilibria (…
Homogenization and Mean-Field Approximation for Multi-Player Games
Rama Cont, Anran Hu
We investigate how the framework of mean-field games may be used to study strategic interactions in large heterogeneous populations. Starting from a partition of the player populat…
Persona-L has Entered the Chat: Leveraging LLM and Ability-based Framework for Personas of People with Complex Needs
Lipeipei Sun, Tianzi Qin, Anran Hu +5
We present Persona-L, a novel approach for creating personas using Large Language Models (LLMs) and an ability-based framework, specifically designed to improve the representation…
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…
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…
Optimization frameworks and sensitivity analysis of Stackelberg mean-field games
Xin Guo, Anran Hu, Jiacheng Zhang
This paper proposes and studies a class of discrete-time finite-time-horizon Stackelberg mean-field games, with one leader and an infinite number of identical and indistinguishable…
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…
Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls
Xin Guo, Anran Hu, Yufei Zhang
We study finite-time horizon continuous-time linear-convex reinforcement learning problems in an episodic setting. In this problem, the unknown linear jump-diffusion process is con…
Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon
Matteo Basei, Xin Guo, Anran Hu +1
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 t…
A General Framework for Learning Mean-Field Games
Xin Guo, Anran Hu, Renyuan Xu +1
This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population. It first establishes the e…
Learning Mean-Field Games
Xin Guo, Anran Hu, Renyuan Xu +1
This paper presents a general mean-field game (GMFG) framework for simultaneous learning and decision-making in stochastic games with a large population. It first establishes the e…
Consistency and Computation of Regularized MLEs for Multivariate Hawkes Processes
Xin Guo, Anran Hu, Renyuan Xu +1
This paper proves the consistency property for the regularized maximum likelihood estimators (MLEs) of multivariate Hawkes processes (MHPs). It also develops an alternating minimiz…