マルチエージェント安全最適制御のための離散GCBF近接方策最適化
Discrete GCBF Proximal Policy Optimization for Multi-agent Safe Optimal Control
未知の離散時間動力学・部分観測・近傍変化・入力制約下で、離散グラフ制御バリア関数と高性能安全方策を同時学習するDGPPOを提案し、複数シミュレータで安全性と性能の両立を示した。
著者: Songyuan Zhang, Oswin So, Mitchell Black, Chuchu Fan
分類: cs.RO, cs.LG, cs.MA, math.OC
原文アブストラクト
Control policies that can achieve high task performance and satisfy safety constraints are desirable for any system, including multi-agent systems (MAS). One promising technique for ensuring the safety of MAS is distributed control barrier functions (CBF). However, it is difficult to design distributed CBF-based policies for MAS that can tackle unknown discrete-time dynamics, partial observability, changing neighborhoods, and input constraints, especially when a distributed high-performance nominal policy that can achieve the task is unavailable. To tackle these challenges, we propose DGPPO, a new framework that simultaneously learns both a discrete graph CBF which handles neighborhood changes and input constraints, and a distributed high-performance safe policy for MAS with unknown discrete-time dynamics. We empirically validate our claims on a suite of multi-agent tasks spanning three different simulation engines. The results suggest that, compared with existing methods, our DGPPO framework obtains policies that achieve high task performance (matching baselines that ignore the safety constraints), and high safety rates (matching the most conservative baselines), with a constant set of hyperparameters across all environments.
