This investigation introduces a novel deep reinforcement learning-based suite to control floating platforms in both simulated and real-world environments. Floating platforms serve as versatile test-beds to emulate microgravity environments on Earth. Our approach addresses the system and environmental uncertainties in controlling such platforms by training policies capable of precise maneuvers amid dynamic and unpredictable conditions. Leveraging state-of-the-art deep reinforcement learning techniques, our suite achieves robustness, adaptability, and good transferability from simulation to reality. Our Deep Reinforcement Learning (DRL) framework provides advantages such as fast training times, large-scale testing capabilities, rich visualization options, and ROS bindings for integration with real-world robotic systems. Beyond policy development, our suite provides a comprehensive platform for researchers, offering open-access at https://github.com/elharirymatteo/RANS/tree/ICRA24.

翻译：本研究提出了一套基于深度强化学习的新方法，用于在仿真和真实环境中控制浮式平台。浮式平台作为在地面模拟微重力环境的通用测试平台。我们的方法通过训练能够在动态及不可预测条件下执行精确机动的策略，解决了控制此类平台时面临的系统与环境不确定性。该套件利用最先进的深度强化学习技术，实现了鲁棒性、适应性以及从仿真到现实的良好迁移能力。该深度强化学习框架具有训练速度快、大规模测试能力强、可视化选项丰富以及可与真实机器人系统集成的ROS绑定等优势。除策略开发外，该套件还为研究人员提供了完整的平台，相关代码已开源发布于https://github.com/elharirymatteo/RANS/tree/ICRA24。