Neural Lyapunov Function Approximation with Self-Supervised Reinforcement Learning

• URL: http://arxiv.org/abs/2503.15629v1
• Date: Wed, 19 Mar 2025 18:29:25 GMT
• Title: Neural Lyapunov Function Approximation with Self-Supervised Reinforcement Learning
• Authors: Luc McCutcheon, Bahman Gharesifard, Saber Fallah,
• Abstract summary: This paper presents a novel, sample-efficient method for neural approximation of nonlinear Lyapunov functions.<n>The proposed approach employs a data-driven World Model to train Lyapunov functions from off-policy trajectories.<n>The method is validated on both standard and goal-conditioned robotic tasks, demonstrating faster convergence and higher approximation accuracy.
• Score: 6.359354545489252
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Control Lyapunov functions are traditionally used to design a controller which ensures convergence to a desired state, yet deriving these functions for nonlinear systems remains a complex challenge. This paper presents a novel, sample-efficient method for neural approximation of nonlinear Lyapunov functions, leveraging self-supervised Reinforcement Learning (RL) to enhance training data generation, particularly for inaccurately represented regions of the state space. The proposed approach employs a data-driven World Model to train Lyapunov functions from off-policy trajectories. The method is validated on both standard and goal-conditioned robotic tasks, demonstrating faster convergence and higher approximation accuracy compared to the state-of-the-art neural Lyapunov approximation baseline. The code is available at: https://github.com/CAV-Research-Lab/SACLA.git

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