Voot Tangkaratt

Publication Activity (10 Years)

Years Active: 2013-2022
Publications (10 Years): 28

Top Topics

Dimension Reduction

Policy Gradient

Entropy Minimization

Reinforcement Learning

Top Venues

Publications

Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information. Neural Networks 152 (2022)
Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama
Robust Imitation Learning from Noisy Demonstrations. AISTATS (2021)
Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
Discovering Diverse Solutions in Deep Reinforcement Learning. CoRR (2021)
Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka
Meta-Model-Based Meta-Policy Optimization. ACML (2021)
Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka
Meta-Model-Based Meta-Policy Optimization. CoRR (2020)
Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
Variational Imitation Learning with Diverse-quality Demonstrations. ICML (2020)
Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama
Robust Imitation Learning from Noisy Demonstrations. CoRR (2020)
Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama
Active deep Q-learning with demonstration. Mach. Learn. 109 (9-10) (2020)
Tatsuya Tanaka, Toshimitsu Kaneko, Masahiro Sekine, Voot Tangkaratt, Masashi Sugiyama
Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning. IROS (2020)
Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. ICLR (Poster) (2019)
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
Imitation Learning from Imperfect Demonstration. CoRR (2019)
Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. CoRR (2019)
Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama
VILD: Variational Imitation Learning with Diverse-quality Demonstrations. CoRR (2019)
Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama
Imitation Learning from Imperfect Demonstration. ICML (2019)
Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan
TD-regularized actor-critic methods. Mach. Learn. 108 (8-9) (2019)
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. CoRR (2018)
Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama
Active Deep Q-learning with Demonstration. CoRR (2018)
Voot Tangkaratt, Abbas Abdolmaleki, Masashi Sugiyama
Guide Actor-Critic for Continuous Control. ICLR (Poster) (2018)
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt, Wu Lin, Yarin Gal, Akash Srivastava
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam. ICML (2018)
Hiroaki Sasaki, Voot Tangkaratt, Gang Niu, Masashi Sugiyama
Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. Neural Comput. 30 (2) (2018)
Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan
TD-Regularized Actor-Critic Methods. CoRR (2018)
Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal
Vprop: Variational Inference using RMSprop. CoRR (2017)
Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama
Policy Search with High-Dimensional Context Variables. AAAI (2017)
Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen
Variational Adaptive-Newton Method for Explorative Learning. CoRR (2017)
Voot Tangkaratt, Hiroaki Sasaki, Masashi Sugiyama
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction. Neural Comput. 29 (8) (2017)
Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama
Policy Search with High-Dimensional Context Variables. CoRR (2016)
Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
Model-based reinforcement learning with dimension reduction. Neural Networks 84 (2016)
Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto
Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning. IEEE Robotics Autom. Mag. 23 (1) (2016)
Voot Tangkaratt, Ning Xie, Masashi Sugiyama
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. Neural Comput. 27 (1) (2015)
Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama
Direct conditional probability density estimation with sparse feature selection. Mach. Learn. 100 (2-3) (2015)
Hiroaki Sasaki, Voot Tangkaratt, Masashi Sugiyama
Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. ACML (2015)
Voot Tangkaratt, Syogo Mori, Tingting Zhao, Jun Morimoto, Masashi Sugiyama
Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation. Neural Networks 57 (2014)
Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto
Efficient reuse of previous experiences in humanoid motor learning. Humanoids (2014)
Voot Tangkaratt, Ning Xie, Masashi Sugiyama
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. CoRR (2014)
Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto
Efficient Reuse of Previous Experiences to Improve Policies in Real Environment. CoRR (2014)
Syogo Mori, Voot Tangkaratt, Tingting Zhao, Jun Morimoto, Masashi Sugiyama
Model-Based Policy Gradients with Parameter-Based Exploration by Least-Squares Conditional Density Estimation. CoRR (2013)
Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration CoRR (2013)
Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama
Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration. Neural Comput. 25 (6) (2013)