Advances in artificial intelligence (AI) have led to its application in many areas of everyday life. In the context of control engineering, reinforcement learning (RL) represents a particularly promising approach as it is centred around the idea of allowing an agent to freely interact with its environment to find an optimal strategy. One of the challenges professionals face when training and deploying RL agents is that the latter often have to run on dedicated embedded devices. This could be to integrate them into an existing toolchain or to satisfy certain performance criteria like real-time constraints. Conventional RL libraries, however, cannot be easily utilised in conjunction with that kind of hardware. In this paper, we present a framework named LExCI, the Learning and Experiencing Cycle Interface, which bridges this gap and provides end-users with a free and open-source tool for training agents on embedded systems using the open-source library RLlib. Its operability is demonstrated with two state-of-the-art RL-algorithms and a rapid control prototyping system.

翻译：人工智能（AI）的进步已使其在日常生活的诸多领域得到应用。在控制工程领域，强化学习（RL）代表了一种特别有前景的方法，其核心思想是允许智能体自由与环境交互以寻找最优策略。专业人员在训练和部署RL智能体时面临的挑战之一是，智能体通常必须在专用嵌入式设备上运行。这可能是为了将其集成到现有工具链中，或是为了满足某些性能标准（如实时性约束）。然而，传统的RL库难以与此类硬件结合使用。本文提出一个名为LExCI（学习与经验循环接口）的框架，该框架弥合了这一鸿沟，并为终端用户提供了一个基于开源库RLlib、在嵌入式系统上训练智能体的免费开源工具。我们通过两种最先进的RL算法和一个快速控制原型系统验证了其可操作性。