Data-driven adaptive exploration achieves O(N^{3/4}) regret in continuous-time linear-quadratic reinforcement learning, matching fixed-schedule methods and extending them to zero initial states.
Solvability and asymptotic behavior of generalized riccati equations arising in indefinite stochastic lq controls,
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Data-Driven Exploration for a Class of Continuous-Time Indefinite Linear--Quadratic Reinforcement Learning Problems
Data-driven adaptive exploration achieves O(N^{3/4}) regret in continuous-time linear-quadratic reinforcement learning, matching fixed-schedule methods and extending them to zero initial states.