Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration. - Tingting Zhao 001; Hirotaka Hachiya; Voot Tangkaratt; Jun Morimoto; Masashi Sugiyama (Neural Comput., 2013)

Efficient reuse of previous experiences in humanoid motor learning. - Norikazu Sugimoto; Voot Tangkaratt; Thijs Wensveen; Tingting Zhao 001; Masashi Sugiyama; Jun Morimoto (Humanoids, 2014)

Efficient Reuse of Previous Experiences to Improve Policies in Real Environment. - Norikazu Sugimoto; Voot Tangkaratt; Thijs Wensveen; Tingting Zhao 001; Masashi Sugiyama; Jun Morimoto (CoRR, 2014)

Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation. - Voot Tangkaratt; Syogo Mori; Tingting Zhao 001; Jun Morimoto; Masashi Sugiyama (Neural Networks, 2014)

Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. - Hiroaki Sasaki; Voot Tangkaratt; Masashi Sugiyama (ACML, 2015)

Direct conditional probability density estimation with sparse feature selection. - Motoki Shiga; Voot Tangkaratt; Masashi Sugiyama (Mach. Learn., 2015)

Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization. - Voot Tangkaratt; Ning Xie 003; Masashi Sugiyama (Neural Comput., 2015)

Model-based reinforcement learning with dimension reduction. - Voot Tangkaratt; Jun Morimoto; Masashi Sugiyama (Neural Networks, 2016)

Trial and Error: Using Previous Experiences as Simulation Models in Humanoid Motor Learning. - Norikazu Sugimoto; Voot Tangkaratt; Thijs Wensveen; Tingting Zhao 001; Masashi Sugiyama; Jun Morimoto (IEEE Robotics Autom. Mag., 2016)

Policy Search with High-Dimensional Context Variables. - Voot Tangkaratt; Herke van Hoof; Simone Parisi; Gerhard Neumann; Jan Peters 001; Masashi Sugiyama (AAAI, 2017)

Variational Adaptive-Newton Method for Explorative Learning. - Mohammad Emtiyaz Khan; Wu Lin; Voot Tangkaratt; Zuozhu Liu; Didrik Nielsen (CoRR, 2017)

Vprop: Variational Inference using RMSprop. - Mohammad Emtiyaz Khan; Zuozhu Liu; Voot Tangkaratt; Yarin Gal (CoRR, 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)

Guide Actor-Critic for Continuous Control. - Voot Tangkaratt; Abbas Abdolmaleki; Masashi Sugiyama (ICLR (Poster), 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 (ICML, 2018)

Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities. - Hiroaki Sasaki; Voot Tangkaratt; Gang Niu 001; Masashi Sugiyama (Neural Comput., 2018)

Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization. - Takayuki Osa; Voot Tangkaratt; Masashi Sugiyama (ICLR (Poster), 2019)

Imitation Learning from Imperfect Demonstration. - Yueh-Hua Wu; Nontawat Charoenphakdee; Han Bao 002; Voot Tangkaratt; Masashi Sugiyama (ICML, 2019)

VILD: Variational Imitation Learning with Diverse-quality Demonstrations. - Voot Tangkaratt; Bo Han 003; Mohammad Emtiyaz Khan; Masashi Sugiyama (CoRR, 2019)

TD-regularized actor-critic methods. - Simone Parisi; Voot Tangkaratt; Jan Peters 001; Mohammad Emtiyaz Khan (Mach. Learn., 2019)

Variational Imitation Learning with Diverse-quality Demonstrations. - Voot Tangkaratt; Bo Han 003; Mohammad Emtiyaz Khan; Masashi Sugiyama (ICML, 2020)

Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning. - Tatsuya Tanaka; Toshimitsu Kaneko; Masahiro Sekine; Voot Tangkaratt; Masashi Sugiyama (IROS, 2020)

Active deep Q-learning with demonstration. - Si-An Chen; Voot Tangkaratt; Hsuan-Tien Lin; Masashi Sugiyama (Mach. Learn., 2020)

Meta-Model-Based Meta-Policy Optimization. - Takuya Hiraoka; Takahisa Imagawa; Voot Tangkaratt; Takayuki Osa; Takashi Onishi; Yoshimasa Tsuruoka (ACML, 2021)

Robust Imitation Learning from Noisy Demonstrations. - Voot Tangkaratt; Nontawat Charoenphakdee; Masashi Sugiyama (AISTATS, 2021)

Discovering Diverse Solutions in Deep Reinforcement Learning. - Takayuki Osa; Voot Tangkaratt; Masashi Sugiyama (CoRR, 2021)

Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information. - Takayuki Osa; Voot Tangkaratt; Masashi Sugiyama (Neural Networks, 2022)