  Recent advancements in single-cell genomics necessitate precision in gene
panel selection to interpret complex biological data effectively. Those methods
aim to streamline the analysis of scRNA-seq data by focusing on the most
informative genes that contribute significantly to the specific analysis task.
Traditional selection methods, which often rely on expert domain knowledge,
embedded machine learning models, or heuristic-based iterative optimization,
are prone to biases and inefficiencies that may obscure critical genomic
signals. Recognizing the limitations of traditional methods, we aim to
transcend these constraints with a refined strategy. In this study, we
introduce an iterative gene panel selection strategy that is applicable to
clustering tasks in single-cell genomics. Our method uniquely integrates
results from other gene selection algorithms, providing valuable preliminary
boundaries or prior knowledge as initial guides in the search space to enhance
the efficiency of our framework. Furthermore, we incorporate the stochastic
nature of the exploration process in reinforcement learning (RL) and its
capability for continuous optimization through reward-based feedback. This
combination mitigates the biases inherent in the initial boundaries and
harnesses RL's adaptability to refine and target gene panel selection
dynamically. To illustrate the effectiveness of our method, we conducted
detailed comparative experiments, case studies, and visualization analysis.
