Takashi Nagata

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Jinwei XingGoogle Cloud AIVerified email at uci.edu
Emre NeftciInstitute Director, Forschungszentrum Jülich; Professor, RWTH AachenVerified email at fz-juelich.de
Xinyun ZouPostdoc in Biology and Biological Engineering, CaltechVerified email at caltech.edu
Jeff KrichmarProfessor of Cognitive Sciences, Professor of Computer Science, University of California, IrvineVerified email at uci.edu
Yasushi KambayashiNippon Institute of TechnologyVerified email at nit.ac.jp
Kexin ChenUniversity of California, IrvineVerified email at uci.edu
Tsutomu KumazawaResearcher, Software Research Associates, Inc.,

Takashi Nagata

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Domain adaptation in reinforcement learning via latent unified state representation J Xing, T Nagata, K Chen, X Zou, E Neftci, JL Krichmar Proceedings of the AAAI Conference on Artificial Intelligence 35 (12), 10452 …, 2021	40	2021
Suppressing the total costs of executing tasks using mobile agents T Nagata, M Takimoto, Y Kambayashi 2009 42nd Hawaii International Conference on System Sciences, 1-10, 2009	23	2009
Cooperatively searching objects based on mobile agents T Nagata, M Takimoto, Y Kambayashi Transactions on Computational Collective Intelligence XI, 119-136, 2013	10	2013
Achieving efficient interpretability of reinforcement learning via policy distillation and selective input gradient regularization J Xing, T Nagata, X Zou, E Neftci, JL Krichmar Neural Networks 161, 228-241, 2023	7	2023
Policy distillation with selective input gradient regularization for efficient interpretability J Xing, T Nagata, X Zou, E Neftci, JL Krichmar arXiv preprint arXiv:2205.08685, 2022	6	2022
Uncertainty Aware Model Integration on Reinforcement Learning T Nagata, J Xing, T Kumazawa, E Neftci 2022 International Joint Conference on Neural Networks (IJCNN), 1-7, 2022		2022
Towards Data Efficiency on Model-Based Reinforcement Learning: Model Confidence and Representation T Nagata University of California, Irvine, 2022		2022

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