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Reinforcement learning improves behaviour from evaluative feedback

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  • Michael L. Littman

    (Brown University)

Abstract

Reinforcement learning is a branch of machine learning concerned with using experience gained through interacting with the world and evaluative feedback to improve a system's ability to make behavioural decisions. It has been called the artificial intelligence problem in a microcosm because learning algorithms must act autonomously to perform well and achieve their goals. Partly driven by the increasing availability of rich data, recent years have seen exciting advances in the theory and practice of reinforcement learning, including developments in fundamental technical areas such as generalization, planning, exploration and empirical methodology, leading to increasing applicability to real-life problems.

Suggested Citation

  • Michael L. Littman, 2015. "Reinforcement learning improves behaviour from evaluative feedback," Nature, Nature, vol. 521(7553), pages 445-451, May.
    • Handle: RePEc:nat:nature:v:521:y:2015:i:7553:d:10.1038_nature14540
    DOI: 10.1038/nature14540
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    Cited by:

    1. Wenjing Guo & Cairong Yan & Ting Lu, 2019. "Optimizing the lifetime of wireless sensor networks via reinforcement-learning-based routing," International Journal of Distributed Sensor Networks, , vol. 15(2), pages 15501477198, February.
    2. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    3. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    4. Vijendra Kumar & Hazi Md. Azamathulla & Kul Vaibhav Sharma & Darshan J. Mehta & Kiran Tota Maharaj, 2023. "The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management," Sustainability, MDPI, vol. 15(13), pages 1-33, July.
    5. Liang, Xuedong & Luo, Peng & Li, Xiaoyan & Wang, Xia & Shu, Lingli, 2023. "Crude oil price prediction using deep reinforcement learning," Resources Policy, Elsevier, vol. 81(C).
    6. Adrian Millea, 2021. "Deep Reinforcement Learning for Trading—A Critical Survey," Data, MDPI, vol. 6(11), pages 1-25, November.
    7. Li, Yanbin & Wang, Jiani & Wang, Weiye & Liu, Chang & Li, Yun, 2023. "Dynamic pricing based electric vehicle charging station location strategy using reinforcement learning," Energy, Elsevier, vol. 281(C).
    8. Chuhan Wu & Fangzhao Wu & Tao Qi & Wei-Qiang Zhang & Xing Xie & Yongfeng Huang, 2022. "Removing AI’s sentiment manipulation of personalized news delivery," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.

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