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Relabeling and policy distillation of hierarchical reinforcement learning

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Abstract

Hierarchical reinforcement learning (HRL) is a promising method to extend traditional reinforcement learning to solve more complex tasks. HRL can solve the problems of long-term reward sparsity and credit assignment. However, the existing HRL methods are trained in specific environments and target tasks each time, resulting in low sample utilization. In addition, the low-level sub-policies of the agent will interfere with each other during the migration process, resulting in poor policy stability. Aiming at the issue above, this paper proposes an HRL method, Relabeling and Policy Distillation of Hierarchical Reinforcement Learning (R-PD-HRL), that integrates meta-learning, shared reward relabeling and policy distillation to accelerate the learning speed and improve the policy stability of the agent. In the training process, a reward relabeling module is introduced to act on the experience buffer. Different reward functions are used to relabel the interaction trajectory for the training of other tasks under the same task distribution. At the low-level, policy distillation technology is used to compress the sub-policies of the low-level, and the interference between the policies is reduced while ensuring the correctness of the original low-level sub-policies. Finally, according to different tasks, the high-level policy calls the low-level optimal policy to complete the decision. In both continuous and discrete state-action environments, experimental results show that compared with other methods, the improved sample utilization of this method greatly accelerates the learning speed, and the success rate is as high as 0.6.

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Acknowledgements

This work is financed by The National Nature Science Foundation of China (61673084) and Project of Liaoning Provincial Department of Education (2021LJKZ1180).

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Authors and Affiliations

  1. Information Engineering Faculty, Dalian University, Dalian, 116622, Liaoning, China

    Qijie Zou, Xiling Zhao, Bing Gao, Shuang Chen, Zhiguo Liu & Zhejie Zhang

  2. Communications and Network Key Laboratory, Dalian University, Dalian, 116600, Liaoning, China

    Zhiguo Liu

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Contributions

The authors of this manuscript have made significant contributions to the research, analysis, and writing of the paper. Below is a summary of each author's specific contributions: Zou: Acted as the main supervisor and project leader of the whole research; Provided financial support for research; Participated in the discussion and decision of the research direction; Important revisions and modifications have been made to the whole paper. Zhao: The whole research framework and experimental scheme are designed. Responsible for setting up experimental framework and experimental operation; Data analysis and statistical processing were carried out. The methods and results of the paper are written. Gao: Literature review and related research were carried out. Assist in the design of research methods and experimental protocols; Some data results were analyzed and interpreted. Mainly responsible for writing the introduction and related work parts of the paper. Chen: Data analysis and statistical processing were carried out. Assist in revising and proofreading the whole paper. Liu: During the revision of the article, many constructive suggestions were given to help us improve the article better. Zhang: Assist in the design and execution of experiments; Some data results were analyzed and interpreted. Participated in the revision and proofreading of the paper. We confirm that all listed authors have read and approved the final version of the manuscript and agree to its submission to the journal. Thank you for considering our manuscript.

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Correspondence to Xiling Zhao.

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Zou, Q., Zhao, X., Gao, B. et al. Relabeling and policy distillation of hierarchical reinforcement learning. Int. J. Mach. Learn. & Cyber. 15, 4923–4939 (2024). https://doi.org/10.1007/s13042-024-02192-6

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