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The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models.

, , , and . NeurIPS, (2020)

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Optimization for Reinforcement Learning: From a single agent to cooperative agents., , , and . IEEE Signal Process. Mag., 37 (3): 123-135 (2020)Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents., , , and . CoRR, (2019)TiAda: A Time-scale Adaptive Algorithm for Nonconvex Minimax Optimization., , and . CoRR, (2022)Provably Convergent Policy Optimization via Metric-aware Trust Region Methods., , , and . CoRR, (2023)Sample Complexity and Overparameterization Bounds for Temporal-Difference Learning With Neural Network Approximation., , , and . IEEE Trans. Autom. Control., 68 (5): 2891-2905 (May 2023)Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction., , , , and . CoRR, (2021)Scalable Bayesian Inference via Particle Mirror Descent., , , and . CoRR, (2015)On the Statistical Efficiency of Mean Field Reinforcement Learning with General Function Approximation., , and . CoRR, (2023)Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization., , , and . CoRR, (2023)Reinforcement Learning with General Utilities: Simpler Variance Reduction and Large State-Action Space., , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 1753-1800. PMLR, (2023)