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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

, , , , , , , and . (2017)cite arxiv:1711.00832Comment: Camera-ready copy of NIPS 2017 paper, including appendix.

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α-Rank: Multi-Agent Evaluation by Evolution., , , , , , , , , and . CoRR, (2019)Neural Replicator Dynamics., , , , , , , , and . CoRR, (2019)Generalization in Mean Field Games by Learning Master Policies., , , , , and . CoRR, (2021)Scaling up Mean Field Games with Online Mirror Descent., , , , , , , and . CoRR, (2021)Reinforcement Learning: The Multi-Player Case. (Apprentissage par Renforcement: Le Cas Multijoueur).. Lille University of Science and Technology, France, (2017)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)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)Generalization in Mean Field Games by Learning Master Policies., , , , , and . AAAI, page 9413-9421. AAAI Press, (2022)Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent., , , , , , and . IJCAI, page 464-470. ijcai.org, (2019)Foolproof Cooperative Learning., , , and . CoRR, (2019)