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Quantum Reinforcement Learning

  • Conference paper
Advances in Natural Computation (ICNC 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3611))

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Abstract

A novel quantum reinforcement learning is proposed through combining quantum theory and reinforcement learning. Inspired by state superposition principle, a framework of state value update algorithm is introduced. The state/action value is represented with quantum state and the probability of action eigenvalue is denoted by probability amplitude, which is updated according to rewards. This approach makes a good tradeoff between exploration and exploitation using probability and can speed up learning. The results of simulated experiment verified its effectiveness and superiority.

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Author information

Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, Anhui, 230027, People’s Republic of China

    Daoyi Dong, Chunlin Chen & Zonghai Chen

Authors
  1. Daoyi Dong
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  2. Chunlin Chen
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  3. Zonghai Chen
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Editor information

Editors and Affiliations

  1. School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore

    Lipo Wang

  2. School of Software, Sun Yat-Sen University, 510275, Guangzhou, China

    Ke Chen

  3. School of Computer Engineering, Nanyang Technological University, BLK N4, 2b-39, Nanyang Avenue, 639798, Singapore

    Yew Soon Ong

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© 2005 Springer-Verlag Berlin Heidelberg

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Dong, D., Chen, C., Chen, Z. (2005). Quantum Reinforcement Learning. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_97

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  • DOI: https://doi.org/10.1007/11539117_97

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28325-6

  • Online ISBN: 978-3-540-31858-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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