BFQ enables single-step noise-to-action mapping in offline RL by dividing flow-path displacements into bootstrappable short-range components learned from marginal velocity.
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Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
BFQ enables single-step noise-to-action mapping in offline RL by dividing flow-path displacements into bootstrappable short-range components learned from marginal velocity.