Temporal Fairness in Multiwinner Voting

no code implementations • 7 Dec 2023 • Edith Elkind, Svetlana Obraztsova, Nicholas Teh

Multiwinner voting captures a wide variety of settings, from parliamentary elections in democratic systems to product placement in online shopping platforms.

Fairness

RPM: Generalizable Behaviors for Multi-Agent Reinforcement Learning

no code implementations • 18 Oct 2022 • Wei Qiu, Xiao Ma, Bo An, Svetlana Obraztsova, Shuicheng Yan, Zhongwen Xu

Despite the recent advancement in multi-agent reinforcement learning (MARL), the MARL agents easily overfit the training environment and perform poorly in the evaluation scenarios where other agents behave differently.

Multi-agent Reinforcement Learning reinforcement-learning +1

Off-Beat Multi-Agent Reinforcement Learning

no code implementations • 27 May 2022 • Wei Qiu, Weixun Wang, Rundong Wang, Bo An, Yujing Hu, Svetlana Obraztsova, Zinovi Rabinovich, Jianye Hao, Yingfeng Chen, Changjie Fan

During execution durations, the environment changes are influenced by, but not synchronised with, action execution.

Multi-agent Reinforcement Learning reinforcement-learning +3

RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents

no code implementations • NeurIPS 2021 • Wei Qiu, Xinrun Wang, Runsheng Yu, Rundong Wang, Xu He, Bo An, Svetlana Obraztsova, Zinovi Rabinovich

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

Multi-agent Reinforcement Learning reinforcement-learning +3

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

no code implementations • 9 Aug 2021 • Wanqi Xue, Wei Qiu, Bo An, Zinovi Rabinovich, Svetlana Obraztsova, Chai Kiat Yeo

Empirical results demonstrate that many state-of-the-art MACRL methods are vulnerable to message attacks, and our method can significantly improve their robustness.

Multi-agent Reinforcement Learning reinforcement-learning +1

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

no code implementations • 16 Feb 2021 • Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich

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

Multi-agent Reinforcement Learning reinforcement-learning +3

RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning

no code implementations • 1 Jan 2021 • Wei Qiu, Xinrun Wang, Runsheng Yu, Xu He, Rundong Wang, Bo An, Svetlana Obraztsova, Zinovi Rabinovich

Centralized training with decentralized execution (CTDE) has become an important paradigm in multi-agent reinforcement learning (MARL).

Multi-agent Reinforcement Learning reinforcement-learning +3

Lie on the Fly: Strategic Voting in an Iterative Preference Elicitation Process

no code implementations • 13 May 2019 • Lihi Dery, Svetlana Obraztsova, Zinovi Rabinovich, Meir Kalech

We also provide a careful voting center which is aware of the possible manipulations and avoids manipulative queries when possible.