離散時間バリア関数によるマルチエージェントPOMDPの安全方策合成
Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functions
マルチエージェント部分観測MDPにおいて、信念空間の離散化や有限メモリに頼らず、離散時間バリア関数で安全を保証する方策を設計する手法を提案した。
著者: Mohamadreza Ahmadi, Andrew Singletary, Joel W. Burdick, Aaron D. Ames
分類: cs.RO
原文アブストラクト
A multi-agent partially observable Markov decision process (MPOMDP) is a modeling paradigm used for high-level planning of heterogeneous autonomous agents subject to uncertainty and partial observation. Despite their modeling efficiency, MPOMDPs have not received significant attention in safety-critical settings. In this paper, we use barrier functions to design policies for MPOMDPs that ensure safety. Notably, our method does not rely on discretization of the belief space, or finite memory. To this end, we formulate sufficient and necessary conditions for the safety of a given set based on discrete-time barrier functions (DTBFs) and we demonstrate that our formulation also allows for Boolean compositions of DTBFs for representing more complicated safe sets. We show that the proposed method can be implemented online by a sequence of one-step greedy algorithms as a standalone safe controller or as a safety-filter given a nominal planning policy. We illustrate the efficiency of the proposed methodology based on DTBFs using a high-fidelity simulation of heterogeneous robots.
