论文标题
使用高斯流程的多代理安全计划
Multi-Agent Safe Planning with Gaussian Processes
论文作者
论文摘要
多代理安全系统已成为越来越重要的研究领域，因为我们现在可以轻松地拥有多个AI驱动的系统一起运行。在这种情况下，我们需要确保不仅要确保每个人的安全，而且还要确保整体系统的安全。在本文中，我们介绍了一种新型的多试验安全学习算法，该算法在环境中有多种不同的代理时可以分散的安全导航。该算法对其他代理进行了温和的假设，并以分散的方式接受了训练，即对其他代理商的政策的先验知识很少。实验表明，在优化各种目标时，我们的算法在运行其他算法的机器人方面表现良好。
Multi-agent safe systems have become an increasingly important area of study as we can now easily have multiple AI-powered systems operating together. In such settings, we need to ensure the safety of not only each individual agent, but also the overall system. In this paper, we introduce a novel multi-agent safe learning algorithm that enables decentralized safe navigation when there are multiple different agents in the environment. This algorithm makes mild assumptions about other agents and is trained in a decentralized fashion, i.e. with very little prior knowledge about other agents' policies. Experiments show our algorithm performs well with the robots running other algorithms when optimizing various objectives.
