RL-Studio: A System for Multi-Phase Reinforcement Learning Experimentation
Whiyoung Jung, Sunghoon Hong, Deunsol Yoon, Jeonghye Kim, Yongjae Shin, Suhyun Jung, Hyundam Yoo, Youngjin Kim
Abstract
Reinforcement learning (RL) has evolved beyond monolithic training, yet existing frameworks remain limited to single algorithms or simple offline-to-online transitions. We present multi-phase RL, a framework that orchestrates multiple learning phases for continual policy improvement. It enables efficient fine-tuning of pretrained policies with new data and smooth adaptation from simulation to real-world environments. To support this paradigm, we introduce RL-Studio, a platform that addresses key implementation barriers, including neural architecture mismatches, parameter transfer complexities, and experiment management overhead. It provides phase orchestration, transition-point monitoring, and full experiment lineage tracking. We demonstrate the effectiveness of multi-phase RL through representative scenarios and highlight RL-Studio’s capabilities.
BibTeX
@inproceedings{aaai2026_rlstudioasystemf,
title = {RL-Studio: A System for Multi-Phase Reinforcement Learning Experimentation},
author = {Whiyoung Jung and Sunghoon Hong and Deunsol Yoon and Jeonghye Kim and Yongjae Shin and Suhyun Jung and Hyundam Yoo and Youngjin Kim and Chanwoo Moon and Woohyung Lim and Soonyoung Lee and Kanghoon Lee},
booktitle = {AAAI 2026},
year = {2026}
}