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

Abstract

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.

arXiv comment7 pages, 4 figures

References in corpus (2)

• Adam: A Method for Stochastic Optimization
• Benchmarking Deep Reinforcement Learning for Continuous Control

Cited by in corpus (5)

• Deep Learning in Mobile and Wireless Networking: A Survey
• Next-generation Wireless Solutions for the Smart Factory, Smart Vehicles, the Smart Grid and Smart Cities
• PoPS: Policy Pruning and Shrinking for Deep Reinforcement Learning
• The RFML Ecosystem: A Look at the Unique Challenges of Applying Deep Learning to Radio Frequency Applications
• Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems