ニューラル常微分方程式による安定かつ安全な人間整合型強化学習
Stable and Safe Human-aligned Reinforcement Learning through Neural Ordinary Differential Equations
人間とロボットの動きをニューラルODEで予測し、制御バリア関数と制御リアプノフ関数をアクター・クリティックに統合することで、人間整合型タスクにおける安全性と安定性を両立させる強化学習手法を提案した。
著者: Liqun Zhao, Keyan Miao, Konstantinos Gatsis, Antonis Papachristodoulou
分類: cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
Reinforcement learning (RL) excels in applications such as video games, but ensuring safety as well as the ability to achieve the specified goals remains challenging when using RL for real-world problems, such as human-aligned tasks where human safety is paramount. This paper provides safety and stability definitions for such human-aligned tasks, and then proposes an algorithm that leverages neural ordinary differential equations (NODEs) to predict human and robot movements and integrates the control barrier function (CBF) and control Lyapunov function (CLF) with the actor-critic method to help to maintain the safety and stability for human-aligned tasks. Simulation results show that the algorithm helps the controlled robot to reach the desired goal state with fewer safety violations and better sample efficiency compared to other methods in a human-aligned task.
