8 citations · 9 across the 5 of their papers we have counts for
8 papers
Impact of GPU uncertainty on the training of predictive deep neural networks
Maciej Pietrowski, Andrzej Gajda, Takuto Yamamoto +3
[retracted] We found out that the difference was dependent on the Chainer library, and does not replicate with another library (pytorch) which indicates that the results are probab…
Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning
Wendyam Eric Lionel Ilboudo, Taisuke Kobayashi, Kenji Sugimoto
Behavioral cloning (BC) bears a high potential for safe and direct transfer of human skills to robots. However, demonstrations performed by human operators often contain noise or i…
Hyperbolically-Discounted Reinforcement Learning on Reward-Punishment Framework
Taisuke Kobayashi
This paper proposes a new reinforcement learning with hyperbolic discounting. Combining a new temporal difference error with the hyperbolic discounting in recursive manner and rewa…
Sample-efficient Gear-ratio Optimization for Biomechanical Energy Harvester
Taisuke Kobayashi, Yutaro Ikawa, Takamitsu Matsubara
The biomechanical energy harvester is expected to harvest the electric energies from human motions. A tradeoff between harvesting energy and keeping the user's natural movements sh…
Deep unfolding-based output feedback control design for linear systems with input saturation
Koki Kobayashi, Masaki Ogura, Taisuke Kobayashi +1
In this paper, we propose a deep unfolding-based framework for the output feedback control of systems with input saturation. Although saturation commonly arises in several practica…
t-Soft Update of Target Network for Deep Reinforcement Learning
Taisuke Kobayashi, Wendyam Eric Lionel Ilboudo
This paper proposes a new robust update rule of target network for deep reinforcement learning (DRL), to replace the conventional update rule, given as an exponential moving averag…