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Organization DEVCOM Army Research Laboratory Reference Code ARL-C-CISD-300154-SIS Description About the Research This research develops computational methods that enable robots to perceive and
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Networks (DNNs) sparsity. The compute-intensive floating-point 32-bit representation represents remaining non-zero valued network parameters. These approaches need to be improved to develop a real-time
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on the energy constraints associated with their mission planning and logistics of operation. The goal of this research is to develop new techniques and algorithms that can better plan the missions of aerial and
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collection of streaming sensor data. This project focuses on utilizing state-of-the-art reinforcement algorithms to 1) dynamically learn from multi-agent actions and context, 2) evaluate the environment and
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engineering, computer science, or related fields Expertise in machine-learning and/or online BCI Advanced programming skills (i.e. Python, Matlab, R) and strong experience in algorithmic design, mathematical