Taisuke Kobayashi | DeepAI

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Featured Co-authors

Takamitsu Matsubara
26 publications
Lana Sinapayen
10 publications
Tetsunari Inamura
7 publications
Wendyam Eric Lionel Ilboudo
4 publications
Shingo Murata
4 publications
Kenji Sugimoto
3 publications
Eiji Watanabe
2 publications
Takahito Enomoto
1 publication
Yutaro Ikawa
1 publication
Maciej Pietrowski
1 publication
Andrzej Gajda
1 publication

∙ 03/08/2023

Soft Actor-Critic Algorithm with Truly Inequality Constraint

Soft actor-critic (SAC) in reinforcement learning is expected to be one ...

0 Taisuke Kobayashi, et al. ∙

∙ 12/21/2022

Reward Bonuses with Gain Scheduling Inspired by Iterative Deepening Search

This paper introduces a novel method of adding intrinsic bonuses to task...

0 Taisuke Kobayashi, et al. ∙

∙ 12/08/2022

Real-time Sampling-based Model Predictive Control based on Reverse Kullback-Leibler Divergence and Its Adaptive Acceleration

Sampling-based model predictive control (MPC) can be applied to versatil...

0 Taisuke Kobayashi, et al. ∙

∙ 08/08/2022

Sparse Representation Learning with Modified q-VAE towards Minimal Realization of World Model

Extraction of low-dimensional latent space from high-dimensional observa...

17 Taisuke Kobayashi, et al. ∙

∙ 03/18/2022

Proximal Policy Optimization with Adaptive Threshold for Symmetric Relative Density Ratio

Deep reinforcement learning (DRL) is one of the promising approaches for...

0 Taisuke Kobayashi, et al. ∙

∙ 02/25/2022

Consolidated Adaptive T-soft Update for Deep Reinforcement Learning

Demand for deep reinforcement learning (DRL) is gradually increased to e...

0 Taisuke Kobayashi, et al. ∙

∙ 02/15/2022

L2C2: Locally Lipschitz Continuous Constraint towards Stable and Smooth Reinforcement Learning

This paper proposes a new regularization technique for reinforcement lea...

0 Taisuke Kobayashi, et al. ∙

∙ 01/18/2022

AdaTerm: Adaptive T-Distribution Estimated Robust Moments towards Noise-Robust Stochastic Gradient Optimizer

As the problems to be optimized with deep learning become more practical...

0 Wendyam Eric Lionel Ilboudo, et al. ∙

∙ 11/29/2021

Towards Autonomous Driving of Personal Mobility with Small and Noisy Dataset using Tsallis-statistics-based Behavioral Cloning

Autonomous driving has made great progress and been introduced in practi...

0 Taisuke Kobayashi, et al. ∙

∙ 09/03/2021

Impact of GPU uncertainty on the training of predictive deep neural networks

[retracted] We found out that the difference was dependent on the Chaine...

12 Maciej Pietrowski, et al. ∙

∙ 08/02/2021

Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning

Behavioral cloning (BC) bears a high potential for safe and direct trans...

0 Wendyam Eric Lionel Ilboudo, et al. ∙

∙ 06/16/2021

Latent Representation in Human-Robot Interaction with Explicit Consideration of Periodic Dynamics

This paper presents a new data-driven framework for analyzing periodic p...

0 Taisuke Kobayashi, et al. ∙

∙ 06/03/2021

Hyperbolically-Discounted Reinforcement Learning on Reward-Punishment Framework

This paper proposes a new reinforcement learning with hyperbolic discoun...

0 Taisuke Kobayashi, et al. ∙

∙ 05/27/2021

Optimistic Reinforcement Learning by Forward Kullback-Leibler Divergence Optimization

This paper addresses a new interpretation of reinforcement learning (RL)...

0 Taisuke Kobayashi, et al. ∙

∙ 04/01/2021

Optimization Algorithm for Feedback and Feedforward Policies towards Robot Control Robust to Sensing Failures

Model-free or learning-based control, in particular, reinforcement learn...

0 Taisuke Kobayashi, et al. ∙

∙ 04/01/2021

Sample-efficient Gear-ratio Optimization for Biomechanical Energy Harvester

The biomechanical energy harvester is expected to harvest the electric e...

0 Taisuke Kobayashi, et al. ∙

∙ 01/19/2021

Mirror-Descent Inverse Kinematics for Box-constrained Joint Space

This paper proposes a new Jacobian-based inverse kinematics (IK) explici...

0 Taisuke Kobayashi, et al. ∙

∙ 10/07/2020

Proximal Policy Optimization with Relative Pearson Divergence

Deep reinforcement learning (DRL) is one of the promising approaches for...

0 Taisuke Kobayashi, et al. ∙

∙ 08/25/2020

t-Soft Update of Target Network for Deep Reinforcement Learning

This paper proposes a new robust update rule of the target network for d...

0 Taisuke Kobayashi, et al. ∙

∙ 08/23/2020

Adaptive and Multiple Time-scale Eligibility Traces for Online Deep Reinforcement Learning

Deep reinforcement learning (DRL) is one of the promising approaches to ...

0 Taisuke Kobayashi, et al. ∙

∙ 07/31/2020

Towards Deep Robot Learning with Optimizer applicable to Non-stationary Problems

This paper proposes a new optimizer for deep learning, named d-AmsGrad. ...

0 Taisuke Kobayashi, et al. ∙

∙ 03/04/2020

q-VAE for Disentangled Representation Learning and Latent Dynamical Systems

This paper proposes a novel variational autoencoder (VAE) derived from T...

0 Taisuke Kobayashi, et al. ∙

∙ 02/29/2020

TAdam: A Robust Stochastic Gradient Optimizer

Machine learning algorithms aim to find patterns from observations, whic...

0 Wendyam Eric Lionel Ilboudo, et al. ∙