Multi-Agent Safe Planning with Gaussian Processes
This addresses safety challenges for AI-powered multi-agent systems, but appears incremental as it builds on existing safe planning methods.
The paper tackles the problem of ensuring safety in multi-agent systems by introducing a decentralized safe learning algorithm for navigation, which performs well with robots using other algorithms in experiments.
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.