RFRL Gym: A Reinforcement Learning Testbed for Cognitive Radio
  Applications
Daniel Rosen Illa Rochez Caleb McIrvin Joshua Lee Kevin D'Alessandro Max Wiecek Nhan Hoang Ramzy Saffarini Sam Philips Vanessa Jones Will Ivey Zavier Harris-Smart Zavion Harris-Smart Zayden Chin Amos Johnson Alyse M. Jones William C. Headley
Radio Frequency Reinforcement Learning (RFRL) is anticipated to be a widely
applicable technology in the next generation of wireless communication systems,
particularly 6G and next-gen military communications. Given this, our research
is focused on developing a tool to promote the development of RFRL techniques
that leverage spectrum sensing. In particular, the tool was designed to address
two cognitive radio applications, specifically dynamic spectrum access and
jamming. In order to train and test reinforcement learning (RL) algorithms for
these applications, a simulation environment is necessary to simulate the
conditions that an agent will encounter within the Radio Frequency (RF)
spectrum. In this paper, such an environment has been developed, herein
referred to as the RFRL Gym. Through the RFRL Gym, users can design their own
scenarios to model what an RL agent may encounter within the RF spectrum as
well as experiment with different spectrum sensing techniques. Additionally,
the RFRL Gym is a subclass of OpenAI gym, enabling the use of third-party ML/RL
Libraries. We plan to open-source this codebase to enable other researchers to
utilize the RFRL Gym to test their own scenarios and RL algorithms, ultimately
leading to the advancement of RL research in the wireless communications
domain. This paper describes in further detail the components of the Gym,
results from example scenarios, and plans for future additions.
  Index Terms-machine learning, reinforcement learning, wireless
communications, dynamic spectrum access, OpenAI gym