activity
20152024
most citedRMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents

16 citations · 27 across the 6 of their papers we have counts for

collaborators

13 papers

cs.LG2024

Adaptive Discounting of Training Time Attacks

Ridhima Bector, Abhay Aradhya, Chai Quek +1

Among the most insidious attacks on Reinforcement Learning (RL) solutions are training-time attacks (TTAs) that create loopholes and backdoors in the learned behaviour. Not limited…

cs.AI2023★ 2 cited

Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning

Rundong Wang, Longtao Zheng, Wei Qiu +7

Recent advances in multi-agent reinforcement learning (MARL) allow agents to coordinate their behaviors in complex environments. However, common MARL algorithms still suffer from s…

cs.MA2022

Off-Beat Multi-Agent Reinforcement Learning

Wei Qiu, Weixun Wang, Rundong Wang +7

We investigate model-free multi-agent reinforcement learning (MARL) in environments where off-beat actions are prevalent, i.e., all actions have pre-set execution durations. During…

cs.LG2021

Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning

Wanqi Xue, Wei Qiu, Bo An +3

Recent studies in multi-agent communicative reinforcement learning (MACRL) have demonstrated that multi-agent coordination can be greatly improved by allowing communication between…

cs.LG2021★ 16 cited

RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents

Wei Qiu, Xinrun Wang, Runsheng Yu +5

Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (…

cs.GT2019

Manipulating Elections by Selecting Issues

Jasper Lu, David Kai Zhang, Zinovi Rabinovich +2

Constructive election control considers the problem of an adversary who seeks to sway the outcome of an electoral process in order to ensure that their favored candidate wins. We c…