113 citations · 148 across the 10 of their papers we have counts for
8 papers · 1 filter
Action-Sufficient Goal Representations
Jinu Hyeon, Woobin Park, Hongjoon Ahn +1
In offline goal-conditioned reinforcement learning (GCRL), hierarchical approaches decompose long-horizon tasks into high-level subgoal prediction and low-level action execution. A…
Option-aware Temporally Abstracted Value for Offline Goal-Conditioned Reinforcement Learning
Hongjoon Ahn, Heewoong Choi, Jisu Han +1
Offline goal-conditioned reinforcement learning (GCRL) offers a practical learning paradigm in which goal-reaching policies are trained from abundant state-action trajectory datase…
Listwise Reward Estimation for Offline Preference-based Reinforcement Learning
Heewoong Choi, Sangwon Jung, Hongjoon Ahn +1
In Reinforcement Learning (RL), designing precise reward functions remains to be a challenge, particularly when aligning with human intent. Preference-based RL (PbRL) was introduce…
Reset & Distill: A Recipe for Overcoming Negative Transfer in Continual Reinforcement Learning
Hongjoon Ahn, Jinu Hyeon, Youngmin Oh +2
We argue that the negative transfer problem occurring when the new task to learn arrives is an important problem that needs not be overlooked when developing effective Continual Re…
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
Hongjoon Ahn, Yongyi Yang, Quan Gan +2
Heterogeneous graph neural networks (GNNs) achieve strong performance on node classification tasks in a semi-supervised learning setting. However, as in the simpler homogeneous GNN…
Continual Learning with Node-Importance based Adaptive Group Sparse Regularization
Sangwon Jung, Hongjoon Ahn, Sungmin Cha +1
We propose a novel regularization-based continual learning method, dubbed as Adaptive Group Sparsity based Continual Learning (AGS-CL), using two group sparsity-based penalties. Ou…
Uncertainty-based Continual Learning with Adaptive Regularization
Hongjoon Ahn, Sungmin Cha, Donggyu Lee +1
We introduce a new neural network-based continual learning algorithm, dubbed as Uncertainty-regularized Continual Learning (UCL), which builds on traditional Bayesian online learni…
Iterative Channel Estimation for Discrete Denoising under Channel Uncertainty
Hongjoon Ahn, Taesup Moon
We propose a novel iterative channel estimation (ICE) algorithm that essentially removes the critical known noisy channel assumption for universal discrete denoising problem. Our a…