Stable Inverse Reinforcement Learning: Policies From Control Lyapunov Landscapes

Learning from expert demonstrations to flexibly program an autonomous system with complex behaviors or to predict an agent's behavior is a powerful tool, especially in collaborative control settings. A common method to solve this problem is inverse reinforcement learning (IRL), where the obse...

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Principais autores:	SAMUEL TESFAZGI, Leonhard Sprandl, Armin Lederer, Sandra Hirche
格式:	Artigo
语言:	Inglês
出版:	IEEE 2024-01-01
丛编:	IEEE Open Journal of Control Systems
主题:	Control Lyapunov function convex optimization inverse optimality inverse reinforcement learning learning from demonstrations sum of squares
在线阅读:	https://ieeexplore.ieee.org/document/10643266/
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