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arXiv Machine Learning By Shreeram Murali, Shankar A. Deka, Dominik Baumann

Computationally efficient safe exploration in reinforcement learning

Read the original on arXiv Machine Learning →

The paper introduces “CoLSafe-MDP”, a reinforcement learning algorithm that ensures safe exploration in constrained Markov decision processes. It replaces computationally heavy Gaussian process methods with a Nadaraya-Watson estimator, achieving constant-time scaling for estimate bounds. The authors evaluate the algorithm on a grid-based environment and on observational Martian terrain data.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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