Voot Tangkaratt

According to our database, Voot Tangkaratt authored at least 27 papers between 2013 and 2022.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2022

Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information.

Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama

Neural Networks, 2022

2021

Discovering Diverse Solutions in Deep Reinforcement Learning.

Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama

CoRR, 2021

Robust Imitation Learning from Noisy Demonstrations.

Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Meta-Model-Based Meta-Policy Optimization.

Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka

Proceedings of the Asian Conference on Machine Learning, 2021

2020

Active deep Q-learning with demonstration.

Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama

Mach. Learn., 2020

Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning.

Tatsuya Tanaka, Toshimitsu Kaneko, Masahiro Sekine, Voot Tangkaratt, Masashi Sugiyama

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2020

Variational Imitation Learning with Diverse-quality Demonstrations.

Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

TD-regularized actor-critic methods.

Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan

Mach. Learn., 2019

VILD: Variational Imitation Learning with Diverse-quality Demonstrations.

Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama

CoRR, 2019

Imitation Learning from Imperfect Demonstration.

Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama

Proceedings of the 36th International Conference on Machine Learning, 2019

Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization.

Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities.

Hiroaki Sasaki, Voot Tangkaratt, Gang Niu, Masashi Sugiyama

Neural Comput., 2018

Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam.

Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava

Proceedings of the 35th International Conference on Machine Learning, 2018

Guide Actor-Critic for Continuous Control.

Voot Tangkaratt, Abbas Abdolmaleki, Masashi Sugiyama

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction.

Voot Tangkaratt, Hiroaki Sasaki, Masashi Sugiyama

Neural Comput., 2017

Vprop: Variational Inference using RMSprop.

Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal

CoRR, 2017

Variational Adaptive-Newton Method for Explorative Learning.

Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen

CoRR, 2017

Policy Search with High-Dimensional Context Variables.

Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017

2016

Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning.

Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto

IEEE Robotics Autom. Mag., 2016

Model-based reinforcement learning with dimension reduction.

Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama

Neural Networks, 2016

2015

Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization.

Voot Tangkaratt, Ning Xie, Masashi Sugiyama

Neural Comput., 2015

Direct conditional probability density estimation with sparse feature selection.

Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama

Mach. Learn., 2015

Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities.

Hiroaki Sasaki, Voot Tangkaratt, Masashi Sugiyama

Proceedings of The 7th Asian Conference on Machine Learning, 2015

2014

Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation.

Voot Tangkaratt, Syogo Mori, Tingting Zhao, Jun Morimoto, Masashi Sugiyama

Neural Networks, 2014

Efficient Reuse of Previous Experiences to Improve Policies in Real Environment.

Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto

CoRR, 2014

Efficient reuse of previous experiences in humanoid motor learning.

Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto

Proceedings of the 14th IEEE-RAS International Conference on Humanoid Robots, 2014

2013

Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration.

Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama

Neural Comput., 2013