AI Bibliography
AI Bibliography |
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Wu, R., Zhao, Y., Clarke, A., & Kendall, A. 2017, A framework using machine vision and deep reinforcement learning for self-learning moving objects in a virtual environment. Paper presented at Naval Post Graduate School. |
| Resource type: Proceedings Article BibTeX citation key: Wu2017a View all bibliographic details  |
Categories: Artificial Intelligence, Cognitive Science, Computer Science, Data Sciences, Decision Theory, Engineering, General, Military Science Subcategories: Autonomous systems, Decision making, Deep learning, Drones, Edge AI, JADC2, Machine learning, Military research, Q-learning, Simulations, Virtual reality Creators: Clarke, Kendall, Wu, Zhao Publisher: Collection: Naval Post Graduate School |
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| Abstract |
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In recent artificial intelligence (AI) research, convolutional neural networks (CNNs) can create artificial agents capable of self-learning. Self-learning autonomous moving objects utilize machine vision techniques based on processing and recognizing objects in digital images. Afterwards, deep reinforcement learning (Deep-RL) is applied to understand and learn intelligent actions and controls. The objective of our research is to study methods and designs on how machine vision and deep machine learning algorithms can be implemented in a virtual world (e.g., a computer game) for moving objects (e.g., vehicles or aircrafts) to improve their navigation and detection of threats in real life. In this paper, we create a framework for generating and using data from computer games to be used in CNNs and Deep-RL to perform intelligent actions. We show the initial results of applying the framework and identify various military applications that may benefit from this research.
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