Reinforcement Learning (RL) stands out in finding optimal strategies for complex tasks without prior knowledge of the dynamics of the system. In this work, we use RL for automatically discovering Quantum Error Correction (QEC) codes and their encoding circuits for a given gate set and error model. Our approach is very general and our implementation is extremely efficient. In particular, we find all possible stabilizer codes and their encoding circuits that correct single errors for up to 15 qubits, for an illustrative gate set. All in all, our framework is a versatile tool for QEC across diverse quantum hardware platforms of interest.