PURE achieves an Õ(√(d_R+d_F)/√N) suboptimality gap, up to horizon factors, in continuous-time RL with general function approximation, and adds low-switching and low-rollout variants.
Logarithmic regret for episodic continuous-time linear-quadratic reinforcement learning over a finite-time horizon
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Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation
PURE achieves an Õ(√(d_R+d_F)/√N) suboptimality gap, up to horizon factors, in continuous-time RL with general function approximation, and adds low-switching and low-rollout variants.