Off Policy Lyapunov Stability in Reinforcement Learning

Fuente: arXiv
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Hauptverfasser: Gill, Sarvan, Constantinescu, Daniela
Format: Preprint
Veröffentlicht: 2025
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author Gill, Sarvan
Constantinescu, Daniela
author_facet Gill, Sarvan
Constantinescu, Daniela
contents Traditional reinforcement learning lacks the ability to provide stability guarantees. More recent algorithms learn Lyapunov functions alongside the control policies to ensure stable learning. However, the current self-learned Lyapunov functions are sample inefficient due to their on-policy nature. This paper introduces a method for learning Lyapunov functions off-policy and incorporates the proposed off-policy Lyapunov function into the Soft Actor Critic and Proximal Policy Optimization algorithms to provide them with a data efficient stability certificate. Simulations of an inverted pendulum and a quadrotor illustrate the improved performance of the two algorithms when endowed with the proposed off-policy Lyapunov function.
format Preprint
id arxiv_https___arxiv_org_abs_2509_09863
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Off Policy Lyapunov Stability in Reinforcement Learning
Gill, Sarvan
Constantinescu, Daniela
Systems and Control
Machine Learning
Robotics
Traditional reinforcement learning lacks the ability to provide stability guarantees. More recent algorithms learn Lyapunov functions alongside the control policies to ensure stable learning. However, the current self-learned Lyapunov functions are sample inefficient due to their on-policy nature. This paper introduces a method for learning Lyapunov functions off-policy and incorporates the proposed off-policy Lyapunov function into the Soft Actor Critic and Proximal Policy Optimization algorithms to provide them with a data efficient stability certificate. Simulations of an inverted pendulum and a quadrotor illustrate the improved performance of the two algorithms when endowed with the proposed off-policy Lyapunov function.
title Off Policy Lyapunov Stability in Reinforcement Learning
topic Systems and Control
Machine Learning
Robotics
url https://arxiv.org/abs/2509.09863