Q-Learning in an Autonomous Rover

Marcus McGuire, Paul Morris, Washington Garcia, Shawn Martin, Nicolas Tutuianu, Elan Barenholtz

Abstract


Robotics researchers often require inexpensive hardware that is freely distributable to the public
domain in large numbers, yet reliable enough for use in different applications without fear
of the hardware itself becoming a burden. In the past, researchers have moved towards robot
simulations, in favor of the lack of hardware and ease of replication. In this paper we introduce
an implementation of Q-Learning, a reinforcement learning technique, as a case study for a new
open-source robotics platform, the Brookstone Rover 2.0. We utilize a Theano-based implementation
of Google DeepMind’s Deep Q-Learning algorithm, as well as OpenCV for the purpose
of state-reduction, and determine its effectiveness in our rovers with a color-seeking “rover-ina-
box” task.


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