2 citations · 6 across the 4 of their papers we have counts for
4 papers
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation
Yuanhao Wang, Qinghua Liu, Yu Bai +1
A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the curse of multiagency, where the description length of the game as well as the complexity of many existing lea…
Optimistic MLE -- A Generic Model-based Algorithm for Partially Observable Sequential Decision Making
Qinghua Liu, Praneeth Netrapalli, Csaba Szepesvári +1
This paper introduces a simple efficient learning algorithms for general sequential decision making. The algorithm combines Optimism for exploration with Maximum Likelihood Estimat…
Dive into Big Model Training
Qinghua Liu, Yuxiang Jiang
The increasing scale of model size and continuous improvement of performance herald the arrival of the Big Model era. In this report, we explore what and how the big model training…
Policy Optimization for Markov Games: Unified Framework and Faster Convergence
Runyu Zhang, Qinghua Liu, Huan Wang +3
This paper studies policy optimization algorithms for multi-agent reinforcement learning. We begin by proposing an algorithm framework for two-player zero-sum Markov Games in the f…