24小时热门版块排行榜

>论坛更新日志 (3960)
>文献求助 (408)
>虫友互识 (345)
>导师招生 (323)
>博后之家 (166)
>考博 (155)
>硕博家园 (154)
>休闲灌水 (141)
>论文投稿 (118)
>基金申请 (90)
>招聘信息布告栏 (55)
>考研 (54)
>教师之家 (52)
>公派出国 (43)
>绿色求助(高悬赏) (38)
>找工作 (37)

返回列表

【有奖交流】积极回复本帖子，参与交流，就有机会分得作者 yaoshunbo 的 7 个金币，回帖就立即获得 1 个金币，每人有 1 次机会

yaoshunbo

木虫 (小有名气)

应助: 0 (幼儿园)
金币: 4241.6
帖子: 85
在线: 128.6小时
虫号: 3264134

[交流] 欢迎引用

Hybrid electric vehicles can achieve better fuel economy than conventional vehicles by utilizing multiple power sources. While these power sources have been controlled by rule-based or optimization-based control algorithms, recent studies have shown that machine learning-based con trol algorithms such as online Deep Reinforcement Learning (DRL) can effectively control the power sources as well. However, the optimization and training processes for the online DRL-based pow ertrain control strategy can be very time and resource intensive. In this paper, a new offline–online hybrid DRL strategy is presented where offline vehicle data are exploited to build an initial model and an online learning algorithm explores a new control policy to further improve the fuel economy. In this manner, it is expected that the agent can learn an environment consisting of the vehicle dynamics in a given driving condition more quickly compared to the online algorithms, which learn the optimal control policy by interacting with the vehicle model from zero initial knowledge. By incorporating a priori offline knowledge, the simulation results show that the proposed approach not only accelerates the learning process and makes the learning process more stable, but also leads to a better fuel economy compared to online only learning algorithms.

With the advancements of data science, machine learning has become a vital tool for improving decision making by using raw data and information as input. Significant results can be seen by using different machine learning techniques in various real-world domains, such as cybersecurity systems, engineering, healthcare, e-commerce, agriculture, etc. [1]
[1] Yao, Z.; Yoon, H.-S.; Hong, Y.-K. Control of Hybrid Electric Vehicle Powertrain Using Offline-Online Hybrid Reinforcement Learning. Energies 2023, 16, 652. https://doi.org/10.3390/en16020652

回复此楼