14 citations · 14 across the 3 of their papers we have counts for
4 papers
Representation Learning for General-sum Low-rank Markov Games
Chengzhuo Ni, Yuda Song, Xuezhou Zhang +2
We study multi-agent general-sum Markov games with nonlinear function approximation. We focus on low-rank Markov games whose transition matrix admits a hidden low-rank structure on…
Off-Policy Fitted Q-Evaluation with Differentiable Function Approximators: Z-Estimation and Inference Theory
Ruiqi Zhang, Xuezhou Zhang, Chengzhuo Ni +1
Off-Policy Evaluation (OPE) serves as one of the cornerstones in Reinforcement Learning (RL). Fitted Q Evaluation (FQE) with various function approximators, especially deep neural…
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
Junyu Zhang, Chengzhuo Ni, Zheng Yu +2
Policy gradient (PG) gives rise to a rich class of reinforcement learning (RL) methods. Recently, there has been an emerging trend to accelerate the existing PG methods such as REI…
Learning to Control in Metric Space with Optimal Regret
Lin F. Yang, Chengzhuo Ni, Mengdi Wang
We study online reinforcement learning for finite-horizon deterministic control systems with {\it arbitrary} state and action spaces. Suppose that the transition dynamics and rewar…