Learning to Control Direct Current Motor for Steering in Real Time via Reinforcement Learning
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Model free techniques have been successful at optimal control of complex systems at an expense of copious amounts of data and computation. However, it is often desired to obtain a control policy in a short period of time with minimal data use and computational burden. To this end, we make use of the NFQ algorithm for steering position control of a golf cart in both a real hardware and a simulated environment that was built from real-world interaction. The controller learns to apply a sequence of voltage signals in the presence of environmental uncertainties and inherent non-linearities that challenge the the control task. We were able to increase the rate of successful control under four minutes in simulation and under 11 minutes in real hardware.
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