论文标题
通过基于方案的编程，机器人技术的强化学习受限
Constrained Reinforcement Learning for Robotics via Scenario-Based Programming
论文作者
论文摘要
深厚的增强学习（DRL）在各种机器人应用中取得了突破性的成功。自然而然的结果是，该范式用于安全至关重要的任务，其中可能涉及人类安全和昂贵的硬件。在这种情况下，至关重要的是优化基于DRL的代理的性能，同时提供其行为的保证。本文介绍了一种新型技术，用于将域专家知识纳入受约束的DRL训练回路中。我们的技术利用了基于方案的编程范式，该范式旨在以简单而直观的方式指定此类知识。我们验证了有关流行的机器人地图导航问题，模拟和实际平台上的方法。我们的实验表明，使用我们的方法利用专家知识极大地提高了代理的安全性和性能。
Deep reinforcement learning (DRL) has achieved groundbreaking successes in a wide variety of robotic applications. A natural consequence is the adoption of this paradigm for safety-critical tasks, where human safety and expensive hardware can be involved. In this context, it is crucial to optimize the performance of DRL-based agents while providing guarantees about their behavior. This paper presents a novel technique for incorporating domain-expert knowledge into a constrained DRL training loop. Our technique exploits the scenario-based programming paradigm, which is designed to allow specifying such knowledge in a simple and intuitive way. We validated our method on the popular robotic mapless navigation problem, in simulation, and on the actual platform. Our experiments demonstrate that using our approach to leverage expert knowledge dramatically improves the safety and the performance of the agent.
