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cs.LG2025
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…
cs.LG2025
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…
cs.LG2024
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…