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Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent., , , , , , and . IJCAI, page 464-470. ijcai.org, (2019)Scalable Deep Reinforcement Learning Algorithms for Mean Field Games., , , , , , , , , and 1 other author(s). ICML, volume 162 of Proceedings of Machine Learning Research, page 12078-12095. PMLR, (2022)Fast computation of Nash Equilibria in Imperfect Information Games., , , , , , , , , and 3 other author(s). ICML, volume 119 of Proceedings of Machine Learning Research, page 7119-7129. PMLR, (2020)α-Rank: Multi-Agent Evaluation by Evolution., , , , , , , , , and . CoRR, (2019)Reinforcement Learning: The Multi-Player Case. (Apprentissage par Renforcement: Le Cas Multijoueur).. Lille University of Science and Technology, France, (2017)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)From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization., , , , , , , , , and 3 other author(s). ICML, volume 139 of Proceedings of Machine Learning Research, page 8525-8535. PMLR, (2021)Human-Machine Dialogue as a Stochastic Game., , , and . SIGDIAL Conference, page 2-11. The Association for Computer Linguistics, (2015)Learning Correlated Equilibria in Mean-Field Games., , , , , , , , , and . CoRR, (2022)