Model-based inverse reinforcement learning for deterministic systems
نویسندگان
چکیده
This paper focuses on the development of an online data-driven model-based inverse reinforcement learning (MBIRL) technique for linear and nonlinear deterministic systems. Input output trajectories agent under observation, attempting to optimize unknown reward function, are used estimate function corresponding optimal value in real-time. To achieve MBIRL using limited data, a novel feedback-driven approach is developed. The feedback policy dynamic model observation estimated from measured data estimates generate synthetic drive MBIRL. Theoretical guarantees ultimate boundedness estimation errors general, convergence zero special cases, derived Lyapunov techniques. Proof concept numerical experiments demonstrates utility developed method solve problems.
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ژورنال
عنوان ژورنال: Automatica
سال: 2022
ISSN: ['1873-2836', '0005-1098']
DOI: https://doi.org/10.1016/j.automatica.2022.110242