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arXiv Machine Learning By Marcell Bartos, Bruce D. Lee, Lenart Treven, Andreas Krause, Florian D\"orfler, Melanie N. Zeilinger

Optimistic Online LQR via Intrinsic Rewards

Read the original on arXiv Machine Learning →

The paper introduces IR‑LQR, an optimistic online linear quadratic regulator that incorporates intrinsic rewards and variance regularization to encourage exploration while maintaining the standard LQR structure. By only adjusting the cost function, IR‑LQR remains computationally simple yet achieves the optimal worst‑case regret rate of √T. The authors validate the method with numerical experiments on aircraft pitch angle control and a UAV example, comparing it to state‑of‑the‑art online LQR algorithms.

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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