A Control Strategy of Autonomous Vehicles based on Deep Reinforcement Learning
文献类型：会议论文
| 作者 | Wei Xia; Huiyun Li; Baopu Li |
| 出版日期 | 2016 |
| 会议名称 | ISCID 2016 |
| 会议地点 | 浙江杭州 |
| 英文摘要 | Deep reinforcement learning has received considerable attention after the outstanding performance of AlphaGo. In this paper, we propose a new control strategy of self-driving vehicles using the deep reinforcement learning model, in which learning with an experience of professional driver and a Q-learning algorithm with filtered experience replay are proposed. Experimental results demonstrate that the proposed model can reduce the time consumption of learning by 71.2%, and the stability increases by about 32%, compared with the existing neural fitted Q-iteration algorithm. |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.siat.ac.cn:8080/handle/172644/10091]  |
| 专题 | 深圳先进技术研究院_集成所 |
| 作者单位 | 2016 |
| 推荐引用方式 GB/T 7714 | Wei Xia,Huiyun Li,Baopu Li. A Control Strategy of Autonomous Vehicles based on Deep Reinforcement Learning[C]. 见:ISCID 2016. 浙江杭州. |
入库方式： OAI收割
来源：深圳先进技术研究院
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