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■論文No. |
CHS21033 |
■ページ数 |
7ページ |
■発行日
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2021/12/07 |
■タイトル |
Developing self-driving car simulation wrapper for deep learning purpose |
■タイトル(英語) |
Developing self-driving car simulation wrapper for deep learning purpose |
■著者名 |
Dharmawan Willy(Kanazawa University),Nambo Hidetaka(Kanazawa University) |
■著者名(英語) |
Willy Dharmawan(Kanazawa University),Hidetaka Nambo(Kanazawa University) |
■価格 |
会員 ¥220 一般 ¥330 |
■書籍種類 |
研究会(論文単位) |
■グループ名 |
【E】センサ・マイクロマシン部門 ケミカルセンサ研究会 |
■本誌 |
2021年12月10日ケミカルセンサ研究会
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■本誌掲載ページ |
19-25ページ |
■原稿種別 |
英語 |
■電子版へのリンク |
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■キーワード |
self-driving car simulator wrapper|self-driving car|reinforcement learning|imitation learning|deep learning|end-to-end learning|self-driving car simulator wrapper|self-driving car|reinforcement learning|imitation learning|deep learning|end-to-end learning |
■要約(日本語) |
Self-driving car simulation is the foundation for self-driving car developers to design deep learning algorithms. Some simulators are available online such as Carla, Autoware, AirSim, Udacity, and many more. Carla pos-sesses a limitless prospect from all these simulators to deploy specific scenario problems in the self-driving car. Nevertheless, the deployment of certain environments requires a complex understanding of the frameworks. Based on this problem, we develop a wrapper that utilizes the Carla simulator and enhances its capability by adding features that facilitate reinforcement and imitation learning algorithm building. We also provide an ex-ample of the implementation of Double Deep Q-Network, to emphasize our set of reward policies. Based on our test, the model can converge and achieve a more stable range of rewards after 78 episodes. |
■要約(英語) |
Self-driving car simulation is the foundation for self-driving car developers to design deep learning algorithms. Some simulators are available online such as Carla, Autoware, AirSim, Udacity, and many more. Carla pos-sesses a limitless prospect from all these simulators to deploy specific scenario problems in the self-driving car. Nevertheless, the deployment of certain environments requires a complex understanding of the frameworks. Based on this problem, we develop a wrapper that utilizes the Carla simulator and enhances its capability by adding features that facilitate reinforcement and imitation learning algorithm building. We also provide an ex-ample of the implementation of Double Deep Q-Network, to emphasize our set of reward policies. Based on our test, the model can converge and achieve a more stable range of rewards after 78 episodes. |
■版 型 |
A4 |
■PDFファイルサイズ |
1,718Kバイト |
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