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Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium.

, , , and . Math. Oper. Res., 48 (1): 433-462 (February 2023)

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One Objective to Rule Them All: A Maximization Objective Fusing Estimation and Planning for Exploration., , , , , , , , and . CoRR, (2023)Towards General Function Approximation in Zero-Sum Markov Games., , , and . CoRR, (2021)Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate., , , and . CoRR, (2020)Misspecified nonconvex statistical optimization for sparse phase retrieval., , , , , and . Math. Program., 176 (1-2): 545-571 (2019)Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium., , , and . Math. Oper. Res., 48 (1): 433-462 (February 2023)Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments., , , , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 36593-36604. PMLR, (2023)Doubly Robust Off-Policy Actor-Critic: Convergence and Optimality., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 11581-11591. PMLR, (2021)On Reward-Free RL with Kernel and Neural Function Approximations: Single-Agent MDP and Markov Game., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 8737-8747. PMLR, (2021)Learning While Playing in Mean-Field Games: Convergence and Optimality., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 11436-11447. PMLR, (2021)Provably Efficient Neural GTD for Off-Policy Learning., , , and . NeurIPS, (2020)