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Sample Complexity and Overparameterization Bounds for Temporal-Difference Learning With Neural Network Approximation., , , и . IEEE Trans. Autom. Control., 68 (5): 2891-2905 (мая 2023)Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization., , , и . CoRR, (2023)Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space., , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 1753-1800. PMLR, (2023)Kernel Conditional Moment Constraints for Confounding Robust Inference., и . AISTATS, том 206 из Proceedings of Machine Learning Research, стр. 650-674. PMLR, (2023)Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents., , , и . CoRR, (2019)Parameter-Agnostic Optimization under Relaxed Smoothness., , , и . AISTATS, том 238 из Proceedings of Machine Learning Research, стр. 4861-4869. PMLR, (2024)Taming Nonconvex Stochastic Mirror Descent with General Bregman Divergence., и . AISTATS, том 238 из Proceedings of Machine Learning Research, стр. 3493-3501. PMLR, (2024)Provably Convergent Policy Optimization via Metric-aware Trust Region Methods., , , и . Trans. Mach. Learn. Res., (2023)Periodic Q-Learning., и . L4DC, том 120 из Proceedings of Machine Learning Research, стр. 582-598. PMLR, (2020)Efficiently Escaping Saddle Points for Non-Convex Policy Optimization., , , , и . CoRR, (2023)