The electromagnetic inverse task has long been recognized as a challenging research problem, which attracted substantial attention from the microwave community. In this paper, our objective is to explore the intricate relationship between geometric model imaging and radar view angles in Synthetic Aperture Radar (SAR) images, mainly focusing on the inverse problem of radar view angle estimation given a target model. However, the high cost and limited availability of SAR data acquisition, along with background interference and complex imaging mechanisms in SAR images, present significant challenges to the generalization, robustness, and accurate feature extraction of existing methods. To address these issues, we propose an interactive deep reinforcement learning (DRL) framework, which facilitates the interaction between the agent and an embedded electromagnetic simulator environment to simulate a human-like process of angle prediction step-by-step. A large number of experimental results verified that the proposed method can accurately predict the radar perspective of SAR images.