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This paper provides high-probability guarantees for an unprojected linear TD(0) algorithm with Polyak–Ruppert averaging under Markovian sampling, using a single stepsize schedule that achieves both robust curvature-free and fast curvature-dependent convergence rates.
This paper introduces a temporal difference (TD) learning objective for diffusion models that enforces cross-time consistency along the denoising trajectory. It reformulates denoising as a reinforcement learning policy evaluation problem, showing significant improvements in sample quality (FID), especially for few-step samplers.
This paper proposes STHTD-MP, a behavior-induced Mirror-Prox temporal-difference method for faster off-policy prediction in reinforcement learning. It replaces the covariance metric with the behavior-policy Bellman matrix and provides convergence analysis and experimental comparisons.
This paper addresses an open problem in reinforcement learning by providing a counterexample showing that differential temporal difference learning can diverge when using a global clock, despite converging with a local clock, in average-reward settings.