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ORCIDVoot Tangkaratt
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2020 – today
- 2022
[j11]Takayuki Osa
, Voot Tangkaratt, Masashi Sugiyama:
Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information. Neural Networks 152: 90-104 (2022)- 2021
- [c11]Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka:
Meta-Model-Based Meta-Policy Optimization. ACML 2021: 129-144 - [c10]Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama:
Robust Imitation Learning from Noisy Demonstrations. AISTATS 2021: 298-306 - [i16]Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama:
Discovering Diverse Solutions in Deep Reinforcement Learning. CoRR abs/2103.07084 (2021) - 2020
- [j10]Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama:
Active deep Q-learning with demonstration. Mach. Learn. 109(9-10): 1699-1725 (2020) - [c9]Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama:
Variational Imitation Learning with Diverse-quality Demonstrations. ICML 2020: 9407-9417 - [c8]Tatsuya Tanaka, Toshimitsu Kaneko, Masahiro Sekine, Voot Tangkaratt, Masashi Sugiyama:
Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning. IROS 2020: 9705-9711 - [i15]Takuya Hiraoka, Takahisa Imagawa, Voot Tangkaratt, Takayuki Osa, Takashi Onishi, Yoshimasa Tsuruoka:
Meta-Model-Based Meta-Policy Optimization. CoRR abs/2006.02608 (2020) - [i14]Voot Tangkaratt, Nontawat Charoenphakdee, Masashi Sugiyama:
Robust Imitation Learning from Noisy Demonstrations. CoRR abs/2010.10181 (2020)
2010 – 2019
- 2019
- [j9]Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan:
TD-regularized actor-critic methods. Mach. Learn. 108(8-9): 1467-1501 (2019) - [c7]Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama:
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. ICLR (Poster) 2019 - [c6]Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama:
Imitation Learning from Imperfect Demonstration. ICML 2019: 6818-6827 - [i13]Takayuki Osa, Voot Tangkaratt, Masashi Sugiyama:
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. CoRR abs/1901.01365 (2019) - [i12]Yueh-Hua Wu, Nontawat Charoenphakdee, Han Bao, Voot Tangkaratt, Masashi Sugiyama:
Imitation Learning from Imperfect Demonstration. CoRR abs/1901.09387 (2019) - [i11]Voot Tangkaratt, Bo Han, Mohammad Emtiyaz Khan, Masashi Sugiyama:
VILD: Variational Imitation Learning with Diverse-quality Demonstrations. CoRR abs/1909.06769 (2019) - 2018
- [j8]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) - [c5]Voot Tangkaratt, Abbas Abdolmaleki, Masashi Sugiyama:
Guide Actor-Critic for Continuous Control. ICLR (Poster) 2018 - [c4]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: 2616-2625 - [i10]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 abs/1806.04854 (2018) - [i9]Si-An Chen, Voot Tangkaratt, Hsuan-Tien Lin, Masashi Sugiyama:
Active Deep Q-learning with Demonstration. CoRR abs/1812.02632 (2018) - [i8]Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan:
TD-Regularized Actor-Critic Methods. CoRR abs/1812.08288 (2018) - 2017
- [j7]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): 2076-2122 (2017) - [c3]Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama:
Policy Search with High-Dimensional Context Variables. AAAI 2017: 2632-2638 - [i7]Mohammad Emtiyaz Khan, Wu Lin, Voot Tangkaratt, Zuozhu Liu, Didrik Nielsen:
Variational Adaptive-Newton Method for Explorative Learning. CoRR abs/1711.05560 (2017) - [i6]Mohammad Emtiyaz Khan, Zuozhu Liu, Voot Tangkaratt, Yarin Gal:
Vprop: Variational Inference using RMSprop. CoRR abs/1712.01038 (2017) - 2016
- [j6]Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama:
Model-based reinforcement learning with dimension reduction. Neural Networks 84: 1-16 (2016) - [j5]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): 96-105 (2016) - [i5]Voot Tangkaratt, Herke van Hoof, Simone Parisi, Gerhard Neumann, Jan Peters, Masashi Sugiyama:
Policy Search with High-Dimensional Context Variables. CoRR abs/1611.03231 (2016) - 2015
- [j4]Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama:
Direct conditional probability density estimation with sparse feature selection. Mach. Learn. 100(2-3): 161-182 (2015) - [j3]Voot Tangkaratt, Ning Xie, Masashi Sugiyama:
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. Neural Comput. 27(1): 228-254 (2015) - [c2]Hiroaki Sasaki, Voot Tangkaratt, Masashi Sugiyama:
Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. ACML 2015: 33-48 - 2014
- [j2]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: 128-140 (2014) - [c1]Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto:
Efficient reuse of previous experiences in humanoid motor learning. Humanoids 2014: 554-559 - [i4]Voot Tangkaratt, Ning Xie, Masashi Sugiyama:
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. CoRR abs/1404.6876 (2014) - [i3]Norikazu Sugimoto, Voot Tangkaratt, Thijs Wensveen, Tingting Zhao, Masashi Sugiyama, Jun Morimoto:
Efficient Reuse of Previous Experiences to Improve Policies in Real Environment. CoRR abs/1405.2406 (2014) - 2013
- [j1]Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama:
Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration. Neural Comput. 25(6): 1512-1547 (2013) - [i2]Tingting Zhao, Hirotaka Hachiya, Voot Tangkaratt, Jun Morimoto, Masashi Sugiyama:
Efficient Sample Reuse in Policy Gradients with Parameter-based Exploration. CoRR abs/1301.3966 (2013) - [i1]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 abs/1307.5118 (2013)
Coauthor Index
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