5 citations · 6 across the 3 of their papers we have counts for
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cs.LG2022
Policy Distillation with Selective Input Gradient Regularization for Efficient Interpretability
Jinwei Xing, Takashi Nagata, Xinyun Zou +2
Although deep Reinforcement Learning (RL) has proven successful in a wide range of tasks, one challenge it faces is interpretability when applied to real-world problems. Saliency m…
cs.LG2021★ 5 cited
Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation
Jinwei Xing, Takashi Nagata, Kexin Chen +3
Despite the recent success of deep reinforcement learning (RL), domain adaptation remains an open problem. Although the generalization ability of RL agents is critical for the real…