Time(s) and GFLOPs savings of single-agent tasks.

<div><p>Deep reinforcement learning has achieved significant success in complex decision-making tasks. However, the high computational cost of policies based on deep neural networks restricts their practical application. Specifically, each decision made by an agent requires a complete ne...

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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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Time(s) and GFLOPs savings of single-agent tasks.

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