On one side, the modern day symmetric-key cryptography protects everything from our online banking to critical infrastructures, and the giant key-space of AES-128 alone around 3.4×10³⁸ possibilities makes the brute-force attack impractical, which makes sense for the development of more sophisticated search techniques. Although traditional combinatorial attacks eliminate keys based on algebraic relationships, they have exponential complexity even for low-degree equations and are not applicable to multiple cipher designs. At the same time, advances in machine learning that have been made recently demonstrate that reinforcement learning agents can learn to control search heuristics, cutting solution times by as much as a factor of ten in closely associated optimization problems and greatly enhanced success rates in side-channel key recovery. In this paper, we bridge by incorporating an RL policy in a combinatorial key-search engine, with partial-key candidates and their statistical properties as states, to guide an order of candidates of favorable branches. Results on several cipher instances demonstrate 14–19 percentage-point improvement in recovery rates and close to two-times speed improvement over standard and pureRL baselines, suggesting a promising path towards more intelligent, automated cryptanalysis.