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Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL.

, , , , , и . NeurIPS, стр. 17913-17926. (2021)

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Yang Yang

Yang Yang

Yang Yang

Yang Yang

Yang Yang

 

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Towards General Function Approximation in Zero-Sum Markov Games., , , и . CoRR, (2021)One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration., , , , , , , , и . CoRR, (2023)Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate., , , и . CoRR, (2020)Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium., , , и . Math. Oper. Res., 48 (1): 433-462 (февраля 2023)Misspecified nonconvex statistical optimization for sparse phase retrieval., , , , , и . Math. Program., 176 (1-2): 545-571 (2019)Adaptive Model Design for Markov Decision Process., , , , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 3679-3700. PMLR, (2022)Is Pessimism Provably Efficient for Offline RL?, , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 5084-5096. PMLR, (2021)Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments., , , , , , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 36593-36604. PMLR, (2023)Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality., , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 11581-11591. PMLR, (2021)On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game., , , и . ICML, том 139 из Proceedings of Machine Learning Research, стр. 8737-8747. PMLR, (2021)