A Reference Architecture of Reinforcement Learning Frameworks

ArXi:2603.06413v1 Announce Type: cross The surge in reinforcement learning (RL) applications gave rise to diverse ing technology, such as RL frameworks. However, the architectural patterns of these frameworks are inconsistent across implementations and there exists no reference architecture (RA) to form a common basis of comparison, evaluation, and integration. To address this gap, we propose an RA of RL frameworks.