5 papers
CORE: Compensable Reward as a Catalyst for Improving Offline RL in Wireless Networks
Lipeng Zu, Hansong Zhou, Yu Qian +4
Real-world wireless data are expensive to collect and often lack sufficient expert demonstrations, causing existing offline RL methods to overfit suboptimal behaviors and exhibit u…
Who Speaks What from Afar: Eavesdropping In-Person Conversations via mmWave Sensing
Shaoying Wang, Hansong Zhou, Yukun Yuan +1
Multi-participant meetings occur across various domains, such as business negotiations and medical consultations, during which sensitive information like trade secrets, business st…
Enhancing Q-Value Updates in Deep Q-Learning via Successor-State Prediction
Lipeng Zu, Hansong Zhou, Xiaonan Zhang
Deep Q-Networks (DQNs) estimate future returns by learning from transitions sampled from a replay buffer. However, the target updates in DQN often rely on next states generated by…
Behavior-Adaptive Q-Learning: A Unifying Framework for Offline-to-Online RL
Lipeng Zu, Hansong Zhou, Xiaonan Zhang
Offline reinforcement learning (RL) enables training from fixed data without online interaction, but policies learned offline often struggle when deployed in dynamic environments d…
FedAR: Addressing Client Unavailability in Federated Learning with Local Update Approximation and Rectification
Chutian Jiang, Hansong Zhou, Xiaonan Zhang +1
Federated learning (FL) enables clients to collaboratively train machine learning models under the coordination of a server in a privacy-preserving manner. One of the main challeng…