A Reference Architecture of Reinforcement Learning Frameworks
| Authors | Xiaoran Liu & Istvan David |
| Year | 2026 |
| Field | AI / ML |
| arXiv | 2603.06413 |
| Download | |
| Categories | cs.SE, cs.AI, cs.LG |
Abstract
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.
Engineering Breakdown
Plain English
This paper addresses a critical gap in the reinforcement learning ecosystem: there's no standard architectural blueprint for RL frameworks, making it hard for engineers to compare, evaluate, or integrate different tools. The authors analyzed 18 production RL frameworks using grounded theory methodology to identify recurring architectural patterns and components, then synthesized these findings into a unified reference architecture (RA). They validated their RA by reconstructing characteristic RL patterns and identified architectural trends across frameworks. The result is a practical blueprint that establishes common terminology and component relationships for RL frameworks, enabling better system design and interoperability.
Core Technical Contribution
The core novelty is the first systematic reference architecture for reinforcement learning frameworks derived from empirical analysis of 18 real-world implementations. Unlike prior work that focused on individual RL algorithms or specific framework features, this paper abstracts architectural patterns that recur across diverse frameworks—identifying standard components, their roles, and how they interact. The grounded theory approach ensures the RA is empirically grounded rather than theoretically imposed, making it actionable for framework design and evaluation. This RA serves as a Rosetta Stone for the fragmented RL framework landscape, enabling architects to reason about framework compatibility and extension points.
How It Works
The methodology follows grounded theory: the authors systematically examine 18 RL frameworks, annotating components and their relationships until architectural patterns stabilize. They identify recurring architectural elements like environment simulators, agent models, training loops, policy storage, and monitoring systems—then map dependencies between these components. The reference architecture abstracts these patterns into a canonical structure showing which components are mandatory, which are optional, and how data and control flow between them. Engineers can then map any existing RL framework onto this RA to understand its structure, or use the RA as a checklist when designing new frameworks. The paper also demonstrates the RA's utility by reconstructing well-known RL algorithms and patterns, proving the architecture captures essential framework structure.
Production Impact
For engineers building RL systems, this RA directly solves framework selection and integration headaches. When evaluating whether to use RLlib, Stable Baselines3, or a custom solution, the RA provides a common vocabulary and component checklist, reducing evaluation time and hidden integration costs. Teams can more easily migrate between frameworks by understanding which components map across them, reducing lock-in risk. The architectural trends identified in the paper guide framework extension: engineers know which components are commonly optimized (e.g., vectorized environments, distributed sampling) and which are less mature. The main trade-off is that adopting the RA requires upfront investment to re-architect existing systems; the payoff comes in reduced future integration friction, clearer architecture documentation, and easier hiring and onboarding of engineers unfamiliar with your specific framework choices.
Limitations and When Not to Use This
The paper's RA is descriptive, not prescriptive—it captures how frameworks are currently built, not necessarily how they should be built, so it may codify suboptimal patterns. The analysis covers 18 frameworks, which is solid, but may miss emerging frameworks or domain-specific RL systems (robotics, autonomous vehicles) that operate under different architectural constraints. The paper doesn't address runtime performance trade-offs explicitly: two frameworks may be architecturally equivalent but have vastly different latency or throughput characteristics, which the RA doesn't capture. Additionally, the RA focuses on batch/episodic RL; online or continual learning frameworks may not fit cleanly into the identified patterns. Follow-up work should validate the RA's predictive power (can it help design better frameworks?) and extend it to cover performance profiles and safety-critical RL systems.
Research Context
This work bridges software architecture research and ML systems engineering. It builds on prior architectural work in ML systems (e.g., reference architectures for ML pipelines) but applies rigorous qualitative methods from software engineering to the RL-specific domain. The paper advances the growing body of ML systems work (MLOps, feature stores, model governance) by establishing architectural foundations for one of the fastest-growing categories of ML systems. It complements recent work on RL benchmarking and standardization (Gymnasium, RL Commons) by providing architectural scaffolding. This opens research directions in RL framework performance profiling, automated framework synthesis, and cross-framework portability layers—all of which depend on a shared understanding of RL architecture.
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