5 citations · 11 across the 17 of their papers we have counts for
18 papers
Uncoupled and Convergent Learning in Monotone Games under Bandit Feedback
Jing Dong, Baoxiang Wang, Yaoliang Yu
We study the problem of no-regret learning algorithms for general monotone and smooth games and their last-iterate convergence properties. Specifically, we investigate the problem…
Convergence to Nash Equilibrium and No-regret Guarantee in (Markov) Potential Games
Jing Dong, Baoxiang Wang, Yaoliang Yu
In this work, we study potential games and Markov potential games under stochastic cost and bandit feedback. We propose a variant of the Frank-Wolfe algorithm with sufficient explo…
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…
Taming the Exponential Action Set: Sublinear Regret and Fast Convergence to Nash Equilibrium in Online Congestion Games
Jing Dong, Jingyu Wu, Siwei Wang +2
The congestion game is a powerful model that encompasses a range of engineering systems such as traffic networks and resource allocation. It describes the behavior of a group of ag…
Online Control with Adversarial Disturbance for Continuous-time Linear Systems
Jingwei Li, Jing Dong, Can Chang +2
We study online control for continuous-time linear systems with finite sampling rates, where the objective is to design an online procedure that learns under non-stochastic noise a…
Online Influence Maximization under Decreasing Cascade Model
Fang Kong, Jize Xie, Baoxiang Wang +2
We study online influence maximization (OIM) under a new model of decreasing cascade (DC). This model is a generalization of the independent cascade (IC) model by considering the c…
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…
Information Design in Multi-Agent Reinforcement Learning
Yue Lin, Wenhao Li, Hongyuan Zha +1
Reinforcement learning (RL) is inspired by the way human infants and animals learn from the environment. The setting is somewhat idealized because, in actual tasks, other agents in…
Diverse Policy Optimization for Structured Action Space
Wenhao Li, Baoxiang Wang, Shanchao Yang +1
Enhancing the diversity of policies is beneficial for robustness, exploration, and transfer in reinforcement learning (RL). In this paper, we aim to seek diverse policies in an und…
Improved Regret Bounds for Linear Adversarial MDPs via Linear Optimization
Fang Kong, Xiangcheng Zhang, Baoxiang Wang +1
Learning Markov decision processes (MDP) in an adversarial environment has been a challenging problem. The problem becomes even more challenging with function approximation, since…
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…
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning
Qi Tian, Kun Kuang, Baoxiang Wang +2
Communication is one of the core components for cooperative multi-agent reinforcement learning (MARL). The communication bandwidth, in many real applications, is always subject to…
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich Features
Shanchao Yang, Kaili Ma, Baoxiang Wang +2
Improving the resilience of a network is a fundamental problem in network science, which protects the underlying system from natural disasters and malicious attacks. This is tradit…
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…