Multi-Agent Safe Planning with Gaussian Processes
arXiv:2008.04452 · doi:10.1109/IROS45743.2020.9341169
Abstract
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.
arXiv comment9 pages, 5 figures. Published at IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020
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