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.
The stability of solutions of linear differential equations
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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.