.png){kind=icon}
返回
Distributed Robust Bandits With Efficient Communication
.png){kind=icon}
PRE
AI
DOI：10.1109/TNSE.2022.3231320.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The Distributed Multi-Armed Bandit (DMAB) is a powerful framework for studying many network problems. The DMAB is typically studied in a paradigm, where signals activate each agent with a fixed probability, and the rewards revealed to agents are assumed to be generated from fixed and unknown distributions, i.e., stochastic rewards, or arbitrarily manipulated by an adversary, i.e., adversarial rewards. However, this paradigm fails to capture the dynamics and uncertainties of many real-world applications, where the signal that activates an agent, may not follow any distribution, and the rewards might be partially stochastic and partially adversarial. Motivated by this, we study the asynchronously stochastic DMAB problem with adversarial corruptions where the agent is activated arbitrarily, and rewards initially sampled from distributions might be corrupted by an adversary. The objectives are to simultaneously minimize the regret and communication cost, while robust to corruption. To address all these issues, we propose a Robust and Distributed Active Arm Elimination algorithm, namely RDAAE, which only needs to transmit one real number (e.g., an arm index, or a reward) per communication. We theoretically prove that the performance of regret and communication cost smoothly degrades when the corruption level increases.
Keyword:
Stochastic processes
Costs
Autonomous aerial vehicles
Adaptation models
Uncertainty
Indexes
Heuristic algorithms
Distributed multi-agent bandit (DMAB)
Adversarial corruptions
Cooperation
Robust learning
期刊
I
IF:
7.9
论文数:
2.6K
被引数:
10.0K
机构
引用论文
LCD Monitors as an Alternative for Precision Demanding Visual Psychophysical Experiments.png)
Perception
PerceptionIF0
PREAI
分享收藏Distributed Machine Learning for Wireless Communication Networks: Techniques, Architectures, and Applications无线通信网络的分布式机器学习: 技术，体系结构和应用
OAAI
OAAI分享收藏分享收藏Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and ApplicationsPROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏Decentralized Multi-Agent Multi-Armed Bandit Learning With Calibration for Multi-Cell Caching具有多单元缓存校准的分散式多智能体多臂Bandit学习OAAI
OAAI分享收藏
查看更多内容