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Policy Search with High-Dimensional Context Variables.

V. Tangkaratt, H. van Hoof, S. Parisi, G. Neumann, J. Peters, and M. Sugiyama. AAAI, page 2632-2638. AAAI Press, (2017)

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Alfred Beck Voot

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Efficient Reuse of Previous Experiences to Improve Policies in Real Environment.N. Sugimoto, V. Tangkaratt, T. Wensveen, T. Zhao, M. Sugiyama, and J. Morimoto. CoRR, (2014)Sufficient Dimension Reduction via Direct Estimation of the Gradients of Logarithmic Conditional Densities.H. Sasaki, V. Tangkaratt, and M. Sugiyama. ACML, volume 45 of JMLR Workshop and Conference Proceedings, page 33-48. JMLR.org, (2015)Meta-Model-Based Meta-Policy Optimization.T. Hiraoka, T. Imagawa, V. Tangkaratt, T. Osa, T. Onishi, and Y. Tsuruoka. ACML, volume 157 of Proceedings of Machine Learning Research, page 129-144. PMLR, (2021)Simultaneous Planning for Item Picking and Placing by Deep Reinforcement Learning.T. Tanaka, T. Kaneko, M. Sekine, V. Tangkaratt, and M. Sugiyama. IROS, page 9705-9711. IEEE, (2020)Discovering diverse solutions in deep reinforcement learning by maximizing state-action-based mutual information.T. Osa, V. Tangkaratt, and M. Sugiyama. Neural Networks, (2022)Efficient Sample Reuse in Policy Gradients with Parameter-Based Exploration.T. Zhao, H. Hachiya, V. Tangkaratt, J. Morimoto, and M. Sugiyama. Neural Comput., 25 (6): 1512-1547 (2013)Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam.M. Khan, D. Nielsen, V. Tangkaratt, W. Lin, Y. Gal, and A. Srivastava. ICML, volume 80 of Proceedings of Machine Learning Research, page 2616-2625. PMLR, (2018)Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization.T. Osa, V. Tangkaratt, and M. Sugiyama. ICLR (Poster), OpenReview.net, (2019)Policy Search with High-Dimensional Context Variables.V. Tangkaratt, H. van Hoof, S. Parisi, G. Neumann, J. Peters, and M. Sugiyama. AAAI, page 2632-2638. AAAI Press, (2017)Variational Imitation Learning with Diverse-quality Demonstrations.V. Tangkaratt, B. Han, M. Khan, and M. Sugiyama. ICML, volume 119 of Proceedings of Machine Learning Research, page 9407-9417. PMLR, (2020)

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Policy Search with High-Dimensional Context Variables.

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Alfred Beck Voot

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Disambiguation of "Tangkaratt, Voot"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Policy Search with High-Dimensional Context Variables.

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Alfred Beck Voot

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Policy Search with High-Dimensional Context Variables.