Computationally efficient safe exploration in reinforcement learning

Abstract:Reinforcement learning in real-life applications requires safety guarantees during exploration. Typical reinforcement learning algorithms do not provide such guarantees, and many modifications that do rely on Gaussian processes (GPs), which have a large computational cost. We propose a computationally lightweight algorithm based on the Nadaraya-Watson estimator that safely explores and optimizes constrained Markov decision processes (MDPs). Our algorithm, \textsc{CoLSafe-MDP}, uses an estimator that scales in constant-time with bounds on the estimates, a significant improvement from its GP-based counterparts that scale cubically with the number of data points. We then evaluate its performance in a grid-based environment and on observational Martian terrain data.

Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY)

arXiv:2609.22919 [cs.LG]

(or arXiv:2609.22919v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.22919 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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