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Training neural network for an obstacle-avoiding autonomous mobile robot using reinforcement learning


R.A. Munasypov, G.A. Saitova, S.S. Moskvichev, T.R. Shakhmametev

In this article we present an approach to the obstacle-avoidance task in unknown environment for autonomous mobile robots using reinforcement learning neural network. Q-learning is one of reinforcement learning methods widely used in autonomous mobile robotics. This method is using to train a neural network controller of a mobile robot providing it with autonomous obstacle-avoiding behavior in unknown environment. Simulation results show that the method allows the robot to achieve an efficient locomotion strategy with no collisions with the environment.

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