PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning Projects

2025-05-22

AI Summary

Existing offline reinforcement learning (Offline RL) research lacks standardized infrastructure for creating, storing, and sharing user-customizable benchmarks—relying instead on static, pre-packaged (s,a,s′,r) datasets, which severely hinders reproducibility and collaborative scalability. To address this, we propose the first collaborative data infrastructure specifically designed for Offline RL. Our system adopts a lightweight Python client–containerized RESTful service architecture, integrating JWT-based authentication, ACM-managed automatic TLS, structured metadata indexing, and fine-grained filtering interfaces (at both episode and transition levels). It enables secure, efficient dataset upload and retrieval, cross-team benchmark reproduction, and flexible, user-defined dataset construction. Empirically, the infrastructure significantly improves standardization, engineering scalability, and scientific reproducibility in Offline RL research.

Technology Category

• Machine Learning: Online Learning & Bandits
• Natural Language Processing: Safety and Robustness
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Offline reinforcement learning (RL) has gained traction as a powerful paradigm for learning control policies from pre-collected data, eliminating the need for costly or risky online interactions. While many open-source libraries offer robust implementations of offline RL algorithms, they all rely on datasets composed of experience tuples consisting of state, action, next state, and reward. Managing, curating, and distributing such datasets requires suitable infrastructure. Although static datasets exist for established benchmark problems, no standardized or scalable solution supports developing and sharing datasets for novel or user-defined benchmarks. To address this gap, we introduce PyTupli, a Python-based tool to streamline the creation, storage, and dissemination of benchmark environments and their corresponding tuple datasets. PyTupli includes a lightweight client library with defined interfaces for uploading and retrieving benchmarks and data. It supports fine-grained filtering at both the episode and tuple level, allowing researchers to curate high-quality, task-specific datasets. A containerized server component enables production-ready deployment with authentication, access control, and automated certificate provisioning for secure use. By addressing key barriers in dataset infrastructure, PyTupli facilitates more collaborative, reproducible, and scalable offline RL research.

Problem

• Lack of standardized infrastructure for offline RL datasets
• Difficulty in managing and sharing custom benchmark datasets
• Need for scalable tools to support collaborative offline RL research

Innovation

• Python-based tool for managing RL datasets
• Lightweight client with upload/download interfaces
• Containerized server with security features