MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics
Shuguang Chu, Zebin Huang, Yutong Li, Mingwei Lin, Dejun Li, Ignacio Carlucho, Yvan R. Petillot, Canjun Yang
Abstract
This study introduces MarineGym, a high-performance reinforcement learning platform tailored for underwater robotics. It aims to address the limitations of existing underwater simulation environments in terms of reinforcement learning compatibility, training efficiency, and standardized benchmarking. MarineGym integrates a proposed GPU-accelerated hydrodynamic plugin based on Isaac Sim, achieving a rollout speed of 250,000 frames per second on a single NVIDIA RTX 3060 GPU. It also provides five models of unmanned underwater vehicles, multiple propulsion systems, and a set of predefined tasks covering core underwater control challenges. Additionally, the domain randomization toolkit allows flexible adjustments of the simulation and task parameters during training to improve the Sim2Real transfer. Further benchmark experiments demonstrate that MarineGym improves training efficiency over existing platforms and supports robust policy adaptation under various perturbations in the marine environment. We expect this platform to drive further advancements in RL research for underwater robotics. For more details about MarineGym and its applications, please visit our project page: https://marine-gym.com/.
BibTeX
@inproceedings{iros2025_marinegymahighpe,
title = {MarineGym: A High-Performance Reinforcement Learning Platform for Underwater Robotics},
author = {Shuguang Chu and Zebin Huang and Yutong Li and Mingwei Lin and Dejun Li and Ignacio Carlucho and Yvan R. Petillot and Canjun Yang},
booktitle = {IROS 2025},
year = {2025}
}