5 citations · 11 across the 17 of their papers we have counts for
5 papers · 1 filter
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning
Qi Tian, Kun Kuang, Furui Liu +1
Offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets, which is an important step toward the deployment of mult…
Online Policy Optimization for Robust MDP
Jing Dong, Jingwei Li, Baoxiang Wang +1
Reinforcement learning (RL) has exceeded human performance in many synthetic settings such as video games and Go. However, real-world deployment of end-to-end RL models is less com…
Algorithms and Theory for Supervised Gradual Domain Adaptation
Jing Dong, Shiji Zhou, Baoxiang Wang +1
The phenomenon of data distribution evolving over time has been observed in a range of applications, calling the needs of adaptive learning algorithms. We thus study the problem of…
Provably Efficient Convergence of Primal-Dual Actor-Critic with Nonlinear Function Approximation
Jing Dong, Li Shen, Yinggan Xu +1
We study the convergence of the actor-critic algorithm with nonlinear function approximation under a nonconvex-nonconcave primal-dual formulation. Stochastic gradient descent ascen…
Differentially Private Temporal Difference Learning with Stochastic Nonconvex-Strongly-Concave Optimization
Canzhe Zhao, Yanjie Ze, Jing Dong +2
Temporal difference (TD) learning is a widely used method to evaluate policies in reinforcement learning. While many TD learning methods have been developed in recent years, little…