207 citations · 833 across the 51 of their papers we have counts for
9 papers · 1 filter
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback
Hang Wang, Sen Lin, Junshan Zhang
The ensemble method is a promising way to mitigate the overestimation issue in Q-learning, where multiple function approximators are used to estimate the action values. It is known…
TRGP: Trust Region Gradient Projection for Continual Learning
Sen Lin, Li Yang, Deliang Fan +1
Catastrophic forgetting is one of the major challenges in continual learning. To address this issue, some existing methods put restrictive constraints on the optimization space of…
Model-Based Offline Meta-Reinforcement Learning with Regularization
Sen Lin, Jialin Wan, Tengyu Xu +2
Existing offline reinforcement learning (RL) methods face a few major challenges, particularly the distributional shift between the learned policy and the behavior policy. Offline…
GROWN: GRow Only When Necessary for Continual Learning
Li Yang, Sen Lin, Junshan Zhang +1
Catastrophic forgetting is a notorious issue in deep learning, referring to the fact that Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning ne…
Continual Learning of Generative Models with Limited Data: From Wasserstein-1 Barycenter to Adaptive Coalescence
Mehmet Dedeoglu, Sen Lin, Zhaofeng Zhang +1
Learning generative models is challenging for a network edge node with limited data and computing power. Since tasks in similar environments share model similarity, it is plausible…
Accelerating Distributed Online Meta-Learning via Multi-Agent Collaboration under Limited Communication
Sen Lin, Mehmet Dedeoglu, Junshan Zhang
Online meta-learning is emerging as an enabling technique for achieving edge intelligence in the IoT ecosystem. Nevertheless, to learn a good meta-model for within-task fast adapta…