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

研究重点是开发一种 RFRL 技术的工具，使用频谱感知来推动其发展，并设计了 RFRL Gym 模拟环境，用于训练和测试 RL 算法以应用于动态频谱访问和干扰等认知无线电应用，并支持第三方机器学习 / 强化学习库。