ガウス過程を用いたマルチエージェント安全計画
Multi-Agent Safe Planning with Gaussian Processes
ガウス過程に基づき、他エージェントのポリシーに関する事前知識が少ない分散型設定で、複数エージェントの安全なナビゲーションを実現する学習アルゴリズムを提案した。
著者: Zheqing Zhu, Erdem Bıyık, Dorsa Sadigh
分類: cs.AI, cs.LG, cs.MA, cs.RO
原文アブストラクト
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.
