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Concave Utility Reinforcement Learning: the Mean-field Game viewpoint., , , , , , , and . CoRR, (2021)Solving N-player dynamic routing games with congestion: a mean field approach., , , , , , , , , and . CoRR, (2021)Concave Utility Reinforcement Learning: The Mean-field Game Viewpoint., , , , , , , and . AAMAS, page 489-497. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)Scaling up Mean Field Games with Online Mirror Descent., , , , , , , and . CoRR, (2021)Generalization in Mean Field Games by Learning Master Policies., , , , , and . CoRR, (2021)Approximating the Core via Iterative Coalition Sampling., , , , , , , , , and 4 other author(s). CoRR, (2024)Generalization in Mean Field Games by Learning Master Policies., , , , , and . AAAI, page 9413-9421. AAAI Press, (2022)Solving N-Player Dynamic Routing Games with Congestion: A Mean-Field Approach., , , , , , , , , and . AAMAS, page 1557-1559. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)Learning Correlated Equilibria in Mean-Field Games., , , , , , , , , and . CoRR, (2022)Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications., , , , , and . NeurIPS, (2020)