AI RESEARCH
ReversedQ: Opportunities for Faster Q-Learning in Episodic Online Reinforcement Learning
arXiv CS.LG
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ArXi:2605.20592v1 Announce Type: new We study model-free Q-learning in finite-horizon episodic Marko Decision Processes (MDPs) with stationary dynamics across episodes. We identify a central issue in nascent model-free posterior-sampling works: the reliance on delayed learning in order to prove theoretical guarantees. In particular, we identify three opportunities for faster learning - (i) value-function update order, (ii) update frequencies, and (iii) value-function initialization.