DSR: optimization of performance lower bound for hierarchical policy with dynamical skill refinement

Dongxiang CHEN , Ying WEN

Front. Comput. Sci. ›› 2026, Vol. 20 ›› Issue (6) : 2006347

PDF (1058KB)
Front. Comput. Sci. ›› 2026, Vol. 20 ›› Issue (6) : 2006347 DOI: 10.1007/s11704-025-50561-3
Artificial Intelligence
LETTER

DSR: optimization of performance lower bound for hierarchical policy with dynamical skill refinement

Author information +
History +
PDF (1058KB)

Graphical abstract

Cite this article

Download citation ▾
Dongxiang CHEN, Ying WEN. DSR: optimization of performance lower bound for hierarchical policy with dynamical skill refinement. Front. Comput. Sci., 2026, 20(6): 2006347 DOI:10.1007/s11704-025-50561-3

登录浏览全文

4963

注册一个新账户 忘记密码

References

[1]

Pertsch K, Lee Y, Lim J. Accelerating reinforcement learning with learned skill priors. In: Proceedings of 2020 Conference on Robot Learning. 2021, 188−204

[2]

Hao C, Weaver C, Tang C, Kawamoto K, Tomizuka M, Zhan W . Skill-critic: refining learned skills for hierarchical reinforcement learning. IEEE Robotics and Automation Letters, 2024, 9( 4): 3625–3632

[3]

Rana K, Xu M, Tidd B, Milford M, Sünderhauf N. Residual skill policies: learning an adaptable skill-based action space for reinforcement learning for robotics. In: Proceedings of the 6th Conference on Robot Learning. 2023, 2095−2104

[4]

Burda Y, Edwards H, Storkey A, Klimov O. Exploration by random network distillation. In: Proceedings of the 7th International Conference on Learning Representations. 2019, 1−17

RIGHTS & PERMISSIONS

Higher Education Press

AI Summary AI Mindmap
PDF (1058KB)

Supplementary files

Highlights

Supplementary Material

764

Accesses

0

Citation

Detail

Sections
Recommended

AI思维导图

/