Computationally efficient safe exploration in reinforcement learning
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 perform...
| Authors | Shreeram Murali, Shankar A. Deka, Dominik Baumann |
|---|---|
| Primary category | cs.LG |
| Published | 19 September 2026 |
| Last updated at source | 19 September 2026 |
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