Learning Quadcopter Maneuvers with Concurrent Methods of Policy Optimization
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- Huang Pei-Hua
- Tokyo Institute of Technology
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- Hasegawa Osamu
- Tokyo Institute of Technology
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<p>This study presents an aerial robotic application of deep reinforcement learning that imparts an asynchronous learning framework and trust region policy optimization to a simulated quad-rotor helicopter (quadcopter) environment. In particular, we optimized a control policy asynchronously through interaction with concurrent instances of the environment. The control system was benchmarked and extended with examples to tackle continuous state-action tasks for the quadcoptor: hovering control and balancing an inverted pole. Performing these maneuvers required continuous actions for sensitive control of small acceleration changes of the quadcoptor, thereby maximizing the scalar reward of the defined tasks. The simulation results demonstrated an enhancement of the learning speed and reliability for the tasks.</p>
収録刊行物
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- Journal of Advanced Computational Intelligence and Intelligent Informatics
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Journal of Advanced Computational Intelligence and Intelligent Informatics 21 (4), 639-649, 2017-07-20
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詳細情報 詳細情報について
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- CRID
- 1390001288091294464
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- NII論文ID
- 130007520151
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- NII書誌ID
- AA12042502
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- ISSN
- 18838014
- 13430130
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- NDL書誌ID
- 028357027
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- 抄録ライセンスフラグ
- 使用不可