This work presents the MarineGym, a high-performance reinforcement learning (RL) platform specifically designed for underwater robotics. It aims to address the limitations of existing underwater simulation environments in terms of RL 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 (UUVs), multiple propulsion systems, and a set of predefined tasks covering core underwater control challenges. Additionally, the DR toolkit allows flexible adjustments of simulation and task parameters during training to improve Sim2Real transfer. Further benchmark experiments demonstrate that MarineGym improves training efficiency over existing platforms and supports robust policy adaptation under various perturbations. We expect this platform could 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/.

翻译：本文介绍了MarineGym，一个专为水下机器人设计的高性能强化学习平台。该平台旨在解决现有水下仿真环境在强化学习兼容性、训练效率和标准化基准测试方面的局限性。MarineGym集成了一个基于Isaac Sim提出的GPU加速流体动力学插件，在单块NVIDIA RTX 3060 GPU上实现了每秒250,000帧的仿真推进速度。平台提供了五款无人水下航行器模型、多种推进系统以及一组覆盖核心水下控制挑战的预定义任务。此外，其动态重配置工具包允许在训练过程中灵活调整仿真与任务参数，以提升仿真到现实的迁移能力。进一步的基准实验表明，MarineGym相比现有平台提升了训练效率，并支持在各种扰动下的鲁棒策略适应。我们期望该平台能够推动水下机器人强化学习研究的进一步发展。有关MarineGym及其应用的更多详情，请访问我们的项目页面：https://marine-gym.com/。