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Walking Motion Learning of Quadrupedal Walking Robot by Profit Sharing That Can Learn Deterministic Policy for POMDPs Environments

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Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

In this paper, walking motion learning of quadrupedal walking robot is realized by the Profit Sharing that can learn deterministic policy for POMDPs environments. In this research, we used the Profit Sharing that can learn deterministic policy for POMDPs environments which can obtain the deterministic policy by using the history of observations. We carried out a series of experiments using quadrupedal walking robot, and confirmed that walking motion learning can be realized by the Profit Sharing that can learn deterministic policy for POMDPs environments.

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References

  1. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press (1998)

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  2. Takamori, Y., Osana, Y.: Profit sharing that can learn deterministic policy for POMDPs environments. In: Proceedings of IEEE International Conference on System, Man and Cybernetics, Anchorage (2011)

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  3. Saito, K., Masuda, S.: Profit Sharing Introducing the Judgement of Incomplete Perception. Transactions of the Japanese Society for Artificial Intelligence 19(5), 379–388 (2004)

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Author information

Authors and Affiliations

  1. Tokyo University of Technology, 1401-1 Katakura, Hachioji, Tokyo, Japan

    Yuya Morino & Yuko Osana

Authors
  1. Yuya Morino
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  2. Yuko Osana
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Morino, Y., Osana, Y. (2014). Walking Motion Learning of Quadrupedal Walking Robot by Profit Sharing That Can Learn Deterministic Policy for POMDPs Environments. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_28

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_28

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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