Skip to main content

Advertisement

Springer Nature Link
Log in

Multi-constraint reinforcement learning in complex robot environments

  • Letter
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Ross K W, Varadarajan R. Markov decision processes with sample path constraints: the communicating case. Operations Research, 1989, 37(5): 780–790

    Article  MathSciNet  MATH  Google Scholar 

  2. Altman E. Constrained Markov Decision Processes: Stochastic Modeling. New York: Routledge, 1999

    MATH  Google Scholar 

  3. Zhang Q, Leng S, Ma X, Liu Q, Wang X, Liang B, Liu Y, Yang J. CVaR-constrained policy optimization for safe reinforcement learning. IEEE Transactions on Neural Networks and Learning Systems, 2024

  4. Schulman J, Levine S, Moritz P, Jordan M, Abbeel P. Trust region policy optimization. In: Proceedings of the 32nd International Conference on International Conference on Machine Learning. 2015, 1889–1897

    Google Scholar 

  5. Yang T Y, Rosca J, Narasimhan K, Ramadge P J. Projection-based constrained policy optimization. In: Proceedings of the 8th International Conference on Learning Representations. 2020

    MATH  Google Scholar 

  6. Makoviychuk V, Wawrzyniak L, Guo Y, Lu M, Storey K, Macklin M, Hoeller D, Rudin N, Allshire A, Handa A, State G. Isaac gym: high performance GPU based physics simulation for robot learning. In: Proceedings of the 35th Conference on Neural Information Processing Systems Track on Datasets and Benchmarks 1. 2021

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (No. 2023JBZX011) and the Aeronautical Science Foundation of China (No. 202300010M5001).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Beijing Jiaotong University, Beijing, 100044, China

    Sheng Han, Hengrui Zhang, Hao Wu, Youfang Lin & Kai Lv

  2. Beijing Key Laboratory of Traffic Data Analysis and Mining, Beijing, 100044, China

    Sheng Han, Hengrui Zhang, Hao Wu, Youfang Lin & Kai Lv

Authors
  1. Sheng Han
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Hengrui Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Hao Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Youfang Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Kai Lv
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Kai Lv.

Ethics declarations

Competing interests The authors declare that they have no competing interests or financial conflicts to disclose.

Electronic Supplementary Material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, S., Zhang, H., Wu, H. et al. Multi-constraint reinforcement learning in complex robot environments. Front. Comput. Sci. 19, 198353 (2025). https://doi.org/10.1007/s11704-024-40682-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-024-40682-6