4 citations · 4 across the 8 of their papers we have counts for
8 papers
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning
Lingwei Zhu, Zheng Chen, Eiji Uchibe +1
Maximum Tsallis entropy (MTE) framework in reinforcement learning has gained popularity recently by virtue of its flexible modeling choices including the widely used Shannon entrop…
-Munchausen Reinforcement Learning
Lingwei Zhu, Zheng Chen, Eiji Uchibe +1
The recently successful Munchausen Reinforcement Learning (M-RL) features implicit Kullback-Leibler (KL) regularization by augmenting the reward function with logarithm of the curr…
Multi-Tier Platform for Cognizing Massive Electroencephalogram
Zheng Chen, Lingwei Zhu, Ziwei Yang +1
An end-to-end platform assembling multiple tiers is built for precisely cognizing brain activities. Being fed massive electroencephalogram (EEG) data, the time-frequency spectrogra…
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring
Lingwei Zhu, Koki Odani, Ziwei Yang +4
Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG). Such methods entail laborious manual fe…
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data
Ziwei Yang, Lingwei Zhu, Zheng Chen +4
Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and classification of cancer subtypes are indispensable for effective clinical treatment. Promising results on…
Geometric Value Iteration: Dynamic Error-Aware KL Regularization for Reinforcement Learning
Toshinori Kitamura, Lingwei Zhu, Takamitsu Matsubara
The recent boom in the literature on entropy-regularized reinforcement learning (RL) approaches reveals that Kullback-Leibler (KL) regularization brings advantages to RL algorithms…