The skip ratio curves of LazyAct with different ϵ in single-agent tasks.

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Hongjie Zhang (136127) (author)
مؤلفون آخرون:	Zhenyu Chen (2359471) (author), Hourui Deng (20685396) (author), Chaosheng Feng (20685399) (author)
منشور في:	2025
الموضوعات:	Medicine Biotechnology Sociology Developmental Biology Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified state skipping branch h ?\ rlkey h ?% 5crlkey establish optimization objectives deep reinforcement learning achieved significant success high computational cost div >< p algorithm significantly reduces computational cost utilize pre tuning techniques practical application policies based minimal impact mappo frameworks making tasks making patterns linear increase lazy actor involve reasoning human decision flops required decision made continuous decision complex decision complete tasks approximately 80 actor network
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The skip ratio curves of <i>LazyAct</i> with different <i>ϵ</i> in single-agent tasks.