Researchers have conducted a finite-time analysis of Q-learning using time-varying policies for discounted Markov decision processes under minimal assumptions, achieving a convergence rate that matches off-policy methods but requires more exploration. This study highlights the balance between exploration and exploitation in on-policy learning and introduces novel analytical techniques to manage time-inhomogeneous noise, potentially applicable to other reinforcement learning algorithms.
Read the full article at arXiv stat.ML
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alinemati1983-6987Written by alinemati1983-6987
