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Federated and Transfer Learning pp 329–356Cite as

A Study on Efficient Reinforcement Learning Through Knowledge Transfer

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Part of the book series: Adaptation, Learning, and Optimization ((ALO,volume 27))

Abstract

Although Reinforcement Learning (RL) algorithms have made impressive progress in learning complex tasks over the past years, there are still prevailing short-comings and challenges. Specifically, the sample-inefficiency and limited adaptation across tasks often make classic RL techniques impractical for real-world applications despite the gained representational power when combining deep neural networks with RL, known as Deep Reinforcement Learning (DRL). Recently, a number of approaches to address those issues have emerged. Many of those solutions are based on smart DRL architectures that enhance single task algorithms with the capability to share knowledge between agents and across tasks by introducing Transfer Learning (TL) capabilities. This survey addresses strategies of knowledge transfer from simple parameter sharing to privacy preserving federated learning and aims at providing a general overview of the field of TL in the DRL domain, establishes a classification framework, and briefly describes representative works in the area.

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Notes

  1. 1.

    https://gym.openai.com/.

  2. 2.

    https://github.com/deepmind/lab.

  3. 3.

    https://github.com/Microsoft/AirSim.

  4. 4.

    https://github.com/Microsoft/malmo.

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Acknowledgements

A. H. R. Costa gratefully acknowledges support from CNPq (grant 310085/2020-9) and Itaú Unibanco S.A. (Data Science Center - C2D). A. H. R. Costa and R. A. C. Bianchi’s work was carried out at the Center for Artificial Intelligence - C4AI (FAPESP grant 2019/07665-4 and support from the IBM Corporation). R. Glatt and F. L. Silva’s portion of the work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE-AC52-07NA27344. Lawrence Livermore National Security, LLC. LLNL-JRNL-790961.

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Authors and Affiliations

  1. Lawrence Livermore National Laboratory, Livermore, CA, USA

    Ruben Glatt & Felipe Leno da Silva

  2. Centro Universitário FEI, São Bernardo do Campo, Brazil

    Reinaldo Augusto da Costa Bianchi

  3. Universidade de São Paulo, São Paulo, Brazil

    Anna Helena Reali Costa

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  1. Ruben Glatt
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  2. Felipe Leno da Silva
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  1. Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada

    Roozbeh Razavi-Far

  2. Department of Computer Science, Western University, London, ON, Canada

    Boyu Wang

  3. University of Alberta and the Alberta Machine Intelligence Institute (Amii), Edmonton, AB, Canada

    Matthew E. Taylor

  4. Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Kowloon, China

    Qiang Yang

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Glatt, R., da Silva, F.L., da Costa Bianchi, R.A., Costa, A.H.R. (2023). A Study on Efficient Reinforcement Learning Through Knowledge Transfer. In: Razavi-Far, R., Wang, B., Taylor, M.E., Yang, Q. (eds) Federated and Transfer Learning. Adaptation, Learning, and Optimization, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-031-11748-0_14

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