Abstract: Deep learning has recently demonstrated strong capabilities in pattern discovery and structural inference, offering new possibilities for algorithm design—a field traditionally reliant on human experience and intuition. This paper focuses on the core objective of automatically learning interpretable algorithmic rules under unsupervised settings, using two representative combinatorial optimization problems as cases: course scheduling problem (CSP) and traveling salesman problem (TSP). We propose two deep reinforcement–based methods, Neural-Greedy and Neural-3-OPT. For CSP, Neural-Greedy autonomously learns three optimal greedy rules from large collections of problem instances: prioritizing courses with the earliest finishing times, the latest starting times, and a hybrid of the two. Its underlying mechanism lies in parameter modulation and neuron cooperation, which precisely focuses on critical temporal features and enables the simultaneous acquisition of multiple rules. For TSP, Neural-3-OPT learns efficient 3-opt heuristic autonomously from large collections of problem instances, achieving optimality gaps as low as 0.00%, 0.08%, and 0.74% on random TSP20, TSP50, and TSP100 datasets, respectively. It significantly outperforms the industrial solver OR-Tools and maintains superior performance on the real-world TSPLIB benchmark. This advantage stems from an intelligent decision mechanism of early-stage precise selection, which prioritizes inefficient edges with substantial impact on global solution quality and identifies reconnection strategies adaptive to the entire iterative process, thereby avoiding myopic greedy choices and ineffective iterations to achieve fast convergence and high-quality solutions. Overall, our results demonstrate that deep learning can not only approach optimal solution quality but also autonomously learn interpretable algorithmic rules, offering a data-driven paradigm for algorithm design and providing important insights into the development of AI-assisted algorithm design.