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Offline reinforcement learning with anderson acceleration for robotic tasks

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Abstract

Offline reinforcement learning (RL) can learn effective policy from a fixed batch of data without interaction. However, the real-world requirements, such as better performance and high sample efficiency, put substantial challenges on current offline RL algorithms. In this paper, we propose a novel offline RL method, Constrained and Conservative Reinforcement Learning with Anderson Acceleration (CCRL-AA), which aims to enable the agent to effectively and efficiently learn from offline demonstration data. In our method, Constrained and Conservative Reinforcement Learning (CCRL) restricts the policy’s actions with respect to a batch of training data and learns a conservative Q-function to make the agent effectively learn from the previously collected demonstrations. The mechanism of Anderson acceleration (AA) is integrated to speed up the learning process and improve sample efficiency. Experiments were conducted on robotic simulation tasks, and the results demonstrate that our method can efficiently learn from given demonstrations and give better performance than several other state-of-the-art methods.

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Acknowledgements

This work is partially supported by National Natural Science Foundation of China (61873008), Beijing Natural Science Foundation (4192010) and National Key R & D Plan (2018YFB1307004).

Funding

This work is partially supported by National Natural Science Foundation of China (61873008), Beijing Natural Science Foundation (4192010) and National Key R & D Plan (2018YFB1307004).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

    Guoyu Zuo, Shuai Huang, Jiangeng Li & Daoxiong Gong

  2. Beijing Key Laboratory of Computing Intelligence and Intelligent Systems, Beijing, 100124, China

    Guoyu Zuo, Shuai Huang, Jiangeng Li & Daoxiong Gong

Authors
  1. Guoyu Zuo
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  2. Shuai Huang
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  3. Jiangeng Li
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  4. Daoxiong Gong
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Correspondence to Jiangeng Li.

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All authors of this paper declare no conflict of interest in this paper and agree to submit this manuscript to the journal of Applied Intelligence.

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Author contributions

All authors contributed to the study conception and design. Material preparation, experimental design and data analysis were performed by Guoyu Zuo and Shuai Huang. Manuscript writing and organization were done by Jiangeng Li. Review and commentary were done by Daoxiong Gong. All authors read and approved this manuscript.

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The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

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The authors declare that they have no conflict of interest. This paper has not been previously published, it is published with the permission of the authors’ institution, and all authors of this paper are responsible for the authenticity of the data in the paper.

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All authors of this paper have been informed of the revision and publication of the paper, have checked all data, figures and tables in the manuscript, and are responsible for their truthfulness and accuracy. Names of all contributing authors: Guoyu Zuo; Shuai Huang; Jiangeng Li; Daoxiong Gong.

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Zuo, G., Huang, S., Li, J. et al. Offline reinforcement learning with anderson acceleration for robotic tasks. Appl Intell 52, 9885–9898 (2022). https://doi.org/10.1007/s10489-021-02953-8

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