A Practical Reinforcement Learning Framework for Automatic Radar Detection

Abstract: At present, the parameters of radar detection rely heavily on manual adjustment and empirical knowledge, resulting in low automation. Traditional manual adjustment methods cannot meet the requirements of modern radars for high efficiency, high precision, and high automation. Therefore, it is necessary to explore a new intelligent radar control learning framework and technology to improve the capability and automation of radar detection. Reinforcement learning is popular in decision task learning, but the shortage of samples in radar control tasks makes it difficult to meet the requirements of reinforcement learning. To address the above issues, we propose a practical radar operation reinforcement learning framework, and integrate offline reinforcement learning and meta-reinforcement learning methods to alleviate the sample requirements of reinforcement learning. Experimental results show that our method has the ability to automatically perform as human in radar detection with real-world settings, thereby promoting the practical application of reinforcement learning in radar operation.

Keywords: meta-reinforcement learning; radar detection; reinforcement learning; offline reinforcement learning