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Object Localization Algorithm Based on Meta-Reinforcement Learning Cover
By: Han Yan and  Hong Jiang  
Open Access
|Mar 2024

References

  1. Mathe S, Pirinen A, Sminchisescu C. Reinforcement learning for visual object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 2894–2902.
  2. Zhou W, Lai J, Liao Y, et al. Meta-reinforcement learning based few-shot speech reconstruction for non-intrusive speech quality assessment [J]. Applied Intelligence, 2023, 53(11):14146–14161.
  3. Yao Hongge, Zhang Wei, Yang Haoqi et al. Joint return target depth of intensive study [J]. Journal of automation, 2023, 49 (5):1089–1098. The DOI: 10.16383/j.aasc200045.
  4. Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning [J]. Advances in neural information processing systems, 2017, 30.
  5. Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks [C]//International conference on machine learning. PMLR, 2017:1126–1135.
  6. Gupta A, Mendonca R, Liu Y X, et al. Meta-reinforcement learning of structured exploration strategies [J]. Advances in neural information processing systems, 2018, 31.
  7. Thrun S, Pratt L. Learning to learn: Introduction and overview [M]//Learning to learn. Boston, MA: Springer US, 1998:3–17.
  8. Ajay, Anurag, et al. “Distributionally adaptive meta reinforcement learning.” Advances in Neural Information Processing Systems 35 (2022):25856–25869.
  9. Duan Y, Schulman J, Chen X, et al. Rl $^ 2$: Fast reinforcement learning via slow reinforcement learning [J]. arXiv preprint arXiv:1611.02779, 2016.
  10. Al-Shedivat M, Bansal T, Burda Y, et al. Continuous adaptation via meta-learning in nonstationary and competitive environments [J]. arXiv preprint arXiv:1710.03641, 2017.
  11. Fakoor R, Chaudhari P, Soatto S, et al. Meta-q-learning [J]. arXiv preprint arXiv:1910.00125, 2019.
  12. Wang Y, Yao Q, Kwok J T, et al. Generalizing from a few examples: A survey on few-shot learning [J]. ACM computing surveys (csur), 2020, 53(3):1–34.
  13. Schoettler G, Nair A, Ojea J A, et al. Meta-reinforcement learning for robotic industrial insertion tasks [C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020: 9728–9735.
  14. Garcia F, Thomas P S. A meta-MDP approach to exploration for lifelong reinforcement learning [J]. Advances in Neural Information Processing Systems, 2019, 32.
  15. Sutton R S, Barto A G. Reinforcement learning: An introduction [M]. MIT press, 2018.
  16. Yu T, Quillen D, He Z, et al. Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning [C]//Conference on robot learning. PMLR, 2020:1094–1100.
Language: English
Page range: 55 - 65
Published on: Mar 16, 2024
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2024 Han Yan, Hong Jiang, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.