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Linear-Quadratic Zero-Sum Mean-Field Type Games: Optimality Conditions and Policy Optimization.

, , , and . CoRR, (2020)

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Relative Discrepancy Does Not Separate Information and Communication Complexity., , , , , and . TOCT, 9 (1): 4:1-4:15 (2016)Extended Learning Graphs for Triangle Finding., , and . STACS, volume 66 of LIPIcs, page 20:1-20:14. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2017)New lower bounds for privacy in communication protocols., , and . Electron. Colloquium Comput. Complex., (2013)Scalable Deep Reinforcement Learning Algorithms for Mean Field Games., , , , , , , , , and 1 other author(s). CoRR, (2022)Convergence Analysis of Machine Learning Algorithms for the Numerical Solution of Mean Field Control and Games: I - The Ergodic Case., and . CoRR, (2019)Policy Optimization for Linear-Quadratic Zero-Sum Mean-Field Type Games., , , and . CDC, page 1038-1043. IEEE, (2020)On the Convergence of Model Free Learning in Mean Field Games., , , , and . AAAI, page 7143-7150. AAAI Press, (2020)Mean Field Games Flock! The Reinforcement Learning Way., , , , , and . IJCAI, page 356-362. ijcai.org, (2021)Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning., , and . CoRR, (2019)Policy iteration method for time-dependent Mean Field Games systems with non-separable Hamiltonians., , and . CoRR, (2021)