4 papers · 1 filter
MEO: Mining Reliable Expert Signals for Offline Reinforcement Learning in Wireless Networks
Lipeng Zu, Hansong Zhou, Yu Qian +4
The operation of next-generation wireless networks increasingly hinges on Deep Reinforcement Learning (DRL) to optimize critical network decisions. Offline RL trains agents using a…
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…
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…
From Static Constraints to Dynamic Adaptation: Sample-Level Constraint Relaxation for Offline-to-Online Reinforcement Learning
Lipeng Zu, Yu Qian, Shayok Chakraborty +1
Offline-to-online reinforcement learning (O2O RL) faces a central challenge between retaining offline conservatism and adapting to online feedback under distribution shift. This ch…