Computationally efficient safe exploration in reinforcement learning
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Computer Science > Machine Learning
Title: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.
| Comments: | 8 pages |
| Subjects: | Machine Learning (cs.LG); Robotics (cs.RO); Systems and Control (eess.SY) |
| Cite as: | arXiv:2609.22919 [cs.LG] |
| (or arXiv:2609.22919v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.22919
arXiv-issued DOI via DataCite (pending registration)
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