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Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar

机译：频谱共享雷达强化学习技术的实验分析

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In this work, we first describe a framework for the application of Reinforcement Learning (RL) control to a radar system that operates in a congested spectral setting. We then compare the utility of several RL algorithms through a discussion of experiments performed on Commercial off-the-shelf (COTS) hardware. Each RL technique is evaluated in terms of convergence, radar detection performance achieved in a congested spectral environment, and the ability to share 100MHz spectrum with an uncooperative communications system. We examine policy iteration, which solves an environment posed as a Markov Decision Process (MDP) by directly solving for a stochastic mapping between environmental states and radar waveforms, as well as Deep RL techniques, which utilize a form of $Q$-Learning to approximate a parameterized function that is used by the radar to select optimal actions. We show that RL techniques are beneficial over a Sense-and-Avoid (SAA) scheme and discuss the conditions under which each approach is most effective.
机译：在这项工作中，我们首先描述将强化学习（RL）控制应用到在拥挤的光谱环境中运行的雷达系统的框架。然后，通过讨论在商用现货（COTS）硬件上进行的实验，我们比较了几种RL算法的效用。评估每种RL技术的收敛性，在拥挤的频谱环境中实现的雷达检测性能以及与不合作的通信系统共享100MHz频谱的能力。我们研究了策略迭代，该迭代通过直接解决环境状态与雷达波形之间的随机映射以及Deep RL技术（利用一种形式的马尔可夫决策过程（MDP））解决了一个环境，该环境被称为马尔可夫决策过程（MDP）。 $ Q $ -学习近似由雷达用于选择最佳动作的参数化函数。我们展示了RL技术优于“感知与避免”（SAA）方案，并讨论了每种方法最有效的条件。

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