The alpha(α) curves of LazyAct with different ϵ in multi-agents tasks.

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Bibliographic Details
Main Author:	Hongjie Zhang (136127) (author)
Other Authors:	Zhenyu Chen (2359471) (author), Hourui Deng (20685396) (author), Chaosheng Feng (20685399) (author)
Published:	2025
Subjects:	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 alpha(<i>α</i>) curves of <i>LazyAct</i> with different <i>ϵ</i> in multi-agents tasks.