Knowledge Commons of Institute of Automation,CAS
Convolutional fitted Q iteration for vision-based control problems | |
Zhao Dongbin; Zhu Yuanheng; Lv Le; Chen Yaran; Zhang Qichao | |
2016-11 | |
会议名称 | The 2016 International Joint Conference on Neural Networks |
会议日期 | 24-29 July 2016 |
会议地点 | Vancouver, BC, Canada |
摘要 | In this paper a deep reinforcement learning (DRL) method is proposed to solve the control problem which takes raw image pixels as input states. A convolutional neural network (CNN) is used to approximate Q functions, termed as Q-CNN. A pretrained network, which is the result of a classification challenge on a vast set of natural images, initializes the parameters of Q-CNN. Such initialization assigns Q-CNN with the features of image representation, so it is more concentrated on the control tasks. The weights are tuned under the scheme of fitted Q iteration (FQI), which is an offline reinforcement learning method with the stable convergence property. To demonstrate the performance, a modified Food-Poison problem is simulated. The agent determines its movements based on its forward view. In the end the algorithm successfully learns a satisfied policy which has better performance than the results of previous researches. |
DOI | 10.1109/IJCNN.2016.7727794 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/14476 |
专题 | 多模态人工智能系统全国重点实验室_深度强化学习 |
作者单位 | he State Key Laboratory of Management and Control for Complex Systems, In- stitution of Automation, Chinese Academy of Sciences, Beijing 100190, China. |
推荐引用方式 GB/T 7714 | Zhao Dongbin,Zhu Yuanheng,Lv Le,et al. Convolutional fitted Q iteration for vision-based control problems[C],2016. |
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07727794.pdf(240KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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