A Reference Architecture of Reinforcement Learning Frameworks

The surge in reinforcement learning (RL) applications gave rise to diverse supporting 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. Through a grounded theory approach, we analyze 18 state-of-the-practice RL frameworks and, by that, we identify recurring architectural components and their relationships, and codify them in an RA. To demonstrate our RA, we reconstruct characteristic RL patterns. Finally, we identify architectural trends, e.g., commonly used components, and outline paths to improving RL frameworks.

💡 Research Summary

This paper, “A Reference Architecture of Reinforcement Learning Frameworks,” addresses a significant gap in the rapidly evolving field of Reinforcement Learning (RL). The authors identify that the proliferation of diverse RL frameworks has led to inconsistent architectural patterns, making it difficult to compare, evaluate, and integrate different solutions. To establish a common foundation, the paper proposes a comprehensive Reference Architecture (RA) derived from an empirical analysis of real-world implementations.

The research employs a Grounded Theory methodology to inductively develop the RA. The authors meticulously analyze 18 widely-used, open-source RL frameworks and environments (such as Gymnasium, RLLib, and Stable Baselines3) by examining their source code, configuration files, and documentation. Through iterative open, axial, and selective coding phases, they identify recurring architectural components and their relationships, continuing the analysis until theoretical saturation is reached.

The resulting RA organizes the common elements into a coherent structure comprising four main component groups:

1. Framework: The top-level user-facing layer, containing the Experiment Orchestrator, which manages high-level experiment configuration, hyperparameter tuning, and benchmarking.
2. Framework Core: The engine that coordinates the learning process. It includes the Framework Orchestrator (for managing the training loop), the Agent (with sub-components like Learner, Buffer, and Function Approximator), and the Environment Core (which manages state, actions, and rewards).
3. Environment: Represents the virtual world with which the agent interacts, consisting of a Simulator and a Model that adapts the simulator for RL.
4. Utilities: Supporting services for operational concerns, including Monitoring & Visualization and Data Persistence components.

To demonstrate the utility and validity of the proposed RA, the authors use it to reconstruct the architectures of characteristic RL patterns, such as single-agent and multi-agent training workflows. Furthermore, the analysis reveals key architectural trends in contemporary RL frameworks, such as the common inclusion of distributed execution coordinators, reliance on external libraries for hyperparameter tuning, and the integration of benchmarking managers.

The paper concludes by outlining paths for improving RL frameworks based on the insights gained from the RA, emphasizing enhanced modularity, interface standardization, and better tooling for lifecycle management. This RA serves as a valuable blueprint for framework developers, a comparative tool for adopters, and a foundational guide for software and ML engineers aiming to build robust, reusable, and maintainable RL systems. All data and analysis materials are published as an Open Research Object to ensure reproducibility.