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27 April 2020 Switching deep reinforcement learning based intelligent online decision making for self-organizing autonomous systems under unstructured environment (Conference Presentation)
Zejian Zhou, Hao Xu
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Proceedings Volume 11425, Unmanned Systems Technology XXII; 114250K (2020) https://doi.org/10.1117/12.2556225
Event: SPIE Defense + Commercial Sensing, 2020, Online Only
Abstract
In this paper, finite horizon intelligent decision-making problem has been investigated for self-organized autonomous systems especially under unstructured environment. According to the latest studies, the uncertainty of environment will seriously affect the effectiveness of decision making especially for autonomous systems. To handle these issues, transfer learning, and deep reinforcement learning has been presented recently. However, those existing Learning algorithms commonly needs a large set of state-space which cause the algorithm to be time-consuming and not suitable for real-time application. Therefore, in this paper, a library of polices trained using Deep Q-Learning under similar environments is built and implemented.
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Zejian Zhou and Hao Xu "Switching deep reinforcement learning based intelligent online decision making for self-organizing autonomous systems under unstructured environment (Conference Presentation)", Proc. SPIE 11425, Unmanned Systems Technology XXII, 114250K (27 April 2020); https://doi.org/10.1117/12.2556225
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KEYWORDS
Switching
Computer simulations
Neural networks
Tolerancing
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