Muon scattering tomography (MST) is a powerful noninvasive imaging technique with significant applications in nuclear material detection and security screening. Traditional MST usually relies on the point of closest approach (PoCA) algorithm to reconstruct images from muon scattering data; however, PoCA often suffers from suboptimal image clarity and resolution. To overcome these challenges, we propose a novel approach that leverages reinforcement learning (RL) to enhance MST reconstruction, termed the \(\mu \) RL-enhanced method. By framing the MST optimization task as an RL problem, we developed an intelligent agent capable of dynamically adjusting the key PoCA parameters. The agent is trained using a multi-objective reward function that guides the optimization toward higher-quality reconstructions. Our experimental results show that the \(\mu \) RL-enhanced method significantly outperforms the traditional PoCA baseline across multiple benchmark metrics. Specifically, the proposed approach on average attains a 307% improvement in the intersection over union (IoU), a 79% increase in the structural similarity index measure (SSIM), and a 8.4% enhancement in the peak signal-to-noise ratio (PSNR) across four experiments. Furthermore, when benchmarked against the maximum likelihood scattering and displacement (MLSD) algorithm, the \(\mu \) RL-enhanced method offers modest gains in PSNR and IoU, together with a one-third increase in SSIM. These improvements demonstrate the enhanced reconstruction accuracy and structural fidelity of the \(\mu \) RL-enhanced method, highlighting its potential to advance MST technologies and their applications.
