A Reference Architecture of Reinforcement Learning Frameworks

2026-03-06

AI Summary

This work addresses the absence of a unified reference architecture in existing reinforcement learning frameworks, which leads to structural inconsistencies and hinders comparability and integration. By applying grounded theory, the study systematically analyzes 18 mainstream reinforcement learning frameworks to identify common components and their interaction patterns, thereby constructing the first comprehensive reference architecture for reinforcement learning. This architecture not only enables the reconstruction of typical design patterns and facilitates analysis of framework evolution trends but also establishes a standardized foundation for the future design, evaluation, and optimization of reinforcement learning frameworks, offering reusable guidance for researchers and practitioners alike.

Technology Category

• Machine Learning: Reinforcement Learning
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent/AI Theories and Architectures

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Responsible Web: Consent frameworks and practices on the web

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.

Problem

• Reinforcement Learning
• Reference Architecture
• Framework Architecture
• Architectural Patterns
• Software Frameworks

Innovation

• Reference Architecture
• Reinforcement Learning Frameworks
• Grounded Theory
• Architectural Patterns
• Component Integration