1 citations · 2 across the 5 of their papers we have counts for
6 papers · 1 filter
DPMAC: Differentially Private Communication for Cooperative Multi-Agent Reinforcement Learning
Canzhe Zhao, Yanjie Ze, Jing Dong +2
Communication lays the foundation for cooperation in human society and in multi-agent reinforcement learning (MARL). Humans also desire to maintain their privacy when communicating…
Semantically Aligned Task Decomposition in Multi-Agent Reinforcement Learning
Wenhao Li, Dan Qiao, Baoxiang Wang +3
The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Autom…
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…
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…
Incentivizing an Unknown Crowd
Jing Dong, Shuai Li, Baoxiang Wang
Motivated by the common strategic activities in crowdsourcing labeling, we study the problem of sequential eliciting information without verification (EIWV) for workers with a hete…