1 citations · 1 across the 7 of their papers we have counts for
9 papers
Scalable Neural Incentive Design with Parameterized Mean-Field Approximation
Nathan Corecco, Batuhan Yardim, Vinzenz Thoma +2
Designing incentives for a multi-agent system to induce a desirable Nash equilibrium is both a crucial and challenging problem appearing in many decision-making domains, especially…
Steering No-Regret Agents in MFGs under Model Uncertainty
Leo Widmer, Jiawei Huang, Niao He
Incentive design is a popular framework for guiding agents' learning dynamics towards desired outcomes by providing additional payments beyond intrinsic rewards. However, most exis…
A Variational Inequality Approach to Independent Learning in Static Mean-Field Games
Batuhan Yardim, Semih Cayci, Niao He
Competitive games involving thousands or even millions of players are prevalent in real-world contexts, such as transportation, communications, and computer networks. However, lear…
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage Perspective
Jiawei Huang, Bingcong Li, Christoph Dann +1
Sample efficiency is critical for online Reinforcement Learning from Human Feedback (RLHF). While existing works investigate sample-efficient online exploration strategies, the pot…
Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning
Batuhan Yardim, Niao He
Mean-field games (MFG) have become significant tools for solving large-scale multi-agent reinforcement learning problems under symmetry. However, the assumption of exact symmetry l…
Learning to Steer Markovian Agents under Model Uncertainty
Jiawei Huang, Vinzenz Thoma, Zebang Shen +2
Designing incentives for an adapting population is a ubiquitous problem in a wide array of economic applications and beyond. In this work, we study how to design additional rewards…
When is Mean-Field Reinforcement Learning Tractable and Relevant?
Batuhan Yardim, Artur Goldman, Niao He
Mean-field reinforcement learning has become a popular theoretical framework for efficiently approximating large-scale multi-agent reinforcement learning (MARL) problems exhibiting…
Model-Based RL for Mean-Field Games is not Statistically Harder than Single-Agent RL
Jiawei Huang, Niao He, Andreas Krause
We study the sample complexity of reinforcement learning (RL) in Mean-Field Games (MFGs) with model-based function approximation that requires strategic exploration to find a Nash…
On the Statistical Efficiency of Mean-Field Reinforcement Learning with General Function Approximation
Jiawei Huang, Batuhan Yardim, Niao He
In this paper, we study the fundamental statistical efficiency of Reinforcement Learning in Mean-Field Control (MFC) and Mean-Field Game (MFG) with general model-based function app…