Hierarchical Reinforcement Learning with Opponent Modeling for Distributed Multi-agent Cooperation

no code implementations • 25 Jun 2022 • Zhixuan Liang, Jiannong Cao, Shan Jiang, Divya Saxena, Huafeng Xu

To tackle the issues, we propose a hierarchical reinforcement learning approach with high-level decision-making and low-level individual control for efficient policy search.

Autonomous Vehicles Decision Making +2

A Real-time Contribution Measurement Method for Participants in Federated Learning

no code implementations • 28 Sep 2020 • Bingjie Yan, Yize Zhou, Boyi Liu, Jun Wang, Yuhan Zhang, Li Liu, Xiaolan Nie, Zhiwei Fan, Zhixuan Liang

However, there is a lack of a sufficiently reasonable contribution measurement mechanism to distribute the reward for each agent.

Federated Learning

FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learning

no code implementations • 8 Sep 2020 • Boyi Liu, Bingjie Yan, Yize Zhou, Zhixuan Liang, Cheng-Zhong Xu

Furthermore, we developed federated learning open-source software based on FedCM.

Federated Learning