You Can Trade Your Experience in Distributed Multi-Agent Multi-Armed Bandits

no code implementations • 2023 IEEE/ACM 31st International Symposium on Quality of Service (IWQoS) 2023 • Guoju Gao, He Huang, Jie Wu, Sijie Huang, Yang Du

In this paper, we propose a transaction-based multi-agent MAB framework, where agents can trade their bandit experience with each other to improve their total individual rewards.

Decision Making Multi-Armed Bandits

Auction-Based Combinatorial Multi-Armed Bandit Mechanisms with Strategic Arms

1 code implementation • IEEE Conference on Computer Communications 2021 • Guoju Gao, He Huang, Mingjun Xiao, Jie Wu, Yu-E Sun, Sheng Zhang

The multi-armed bandit (MAB) model has been deeply studied to solve many online learning problems, such as rate allocation in communication networks, Ad recommendation in social networks, etc.

Computational Efficiency

Combinatorial Multi-Armed Bandit Based Unknown Worker Recruitment in Heterogeneous Crowdsensing

1 code implementation • IEEE INFOCOM 2020 - IEEE Conference on Computer Communications 2020 • Guoju Gao, Jie Wu, Mingjun Xiao, Guoliang Chen

In each round, every task may be covered by more than one recruited workers, but its completion quality only depends on these workers' maximum sensing quality.