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This paper presents research in progress investigating the viability andadaptation of reinforcement learning using deep neural network based functionapproximation for the task of radio control and signal detection in thewireless domain. We demonstrate a successful initial method for radio controlwhich allows naive learning of search without the need for expert features,heuristics, or search strategies. We also introduce Kerlym, an open Keras basedreinforcement learning agent collection for OpenAI's Gym.

Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent