5 citations · 11 across the 17 of their papers we have counts for
8 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…
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…