Deep Reinforcement Learning Radio Control and Signal Detection with KeRLym, a Gym RL Agent
This work addresses radio control and signal detection for wireless communication systems, but it appears incremental as it builds on existing reinforcement learning frameworks.
The paper tackles radio control and signal detection in wireless domains using deep reinforcement learning, demonstrating a method that enables naive learning without expert features or heuristics, and introduces Kerlym, an open-source agent collection for OpenAI Gym.
This paper presents research in progress investigating the viability and adaptation of reinforcement learning using deep neural network based function approximation for the task of radio control and signal detection in the wireless domain. We demonstrate a successful initial method for radio control which allows naive learning of search without the need for expert features, heuristics, or search strategies. We also introduce Kerlym, an open Keras based reinforcement learning agent collection for OpenAI's Gym.