3 citations · 7 across the 26 of their papers we have counts for
5 papers · 1 filter
NashPG: A Policy Gradient Method with Iteratively Refined Regularization for Finding Nash Equilibria
Eason Yu, Tzu Hao Liu, Clément L. Canonne +4
Finding Nash equilibria in two-player zero-sum imperfect-information games remains a central challenge in multi-agent reinforcement learning. Recent multi-round regularization meth…
Position: AI Scaling: From Up to Down and Out
Yunke Wang, Yanxi Li, Chang Xu
AI Scaling has traditionally been synonymous with Scaling Up, which builds larger and more powerful models. However, the growing demand for efficiency, adaptability, and collaborat…
Visual Imitation Learning with Calibrated Contrastive Representation
Yunke Wang, Linwei Tao, Bo Du +2
Adversarial Imitation Learning (AIL) allows the agent to reproduce expert behavior with low-dimensional states and actions. However, challenges arise in handling visual states due…
Imitation Learning from Purified Demonstrations
Yunke Wang, Minjing Dong, Yukun Zhao +2
Imitation learning has emerged as a promising approach for addressing sequential decision-making problems, with the assumption that expert demonstrations are optimal. However, in r…
Unlabeled Imperfect Demonstrations in Adversarial Imitation Learning
Yunke Wang, Bo Du, Chang Xu
Adversarial imitation learning has become a widely used imitation learning framework. The discriminator is often trained by taking expert demonstrations and policy trajectories as…