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A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play, , , , , , , , , and 1 other author(s). Science, 362 (6419): 1140--1144 (2018)A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning, , , , , , , and . (2017)cite arxiv:1711.00832Comment: Camera-ready copy of NIPS 2017 paper, including appendix.Computing Approximate Nash Equilibria and Robust Best-Responses Using Sampling., , and . J. Artif. Intell. Res., (2011)Learning from Demonstrations for Real World Reinforcement Learning., , , , , , , , , and 2 other author(s). CoRR, (2017)Reports of the Workshops Held at the 2019 AAAI Conference on Artificial Intelligence., , , , , , , , , and 27 other author(s). AI Mag., 40 (3): 67-78 (2019)Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization., , , and . CoRR, (2024)A Generalized Training Approach for Multiagent Learning., , , , , , , , , and 5 other author(s). CoRR, (2019)Game Theoretic Rating in N-player general-sum games with Equilibria., , , , , , , and . CoRR, (2022)Evaluating Agents using Social Choice Theory., , , , , , , , and . CoRR, (2023)Online Monte Carlo Counterfactual Regret Minimization for Search in Imperfect Information Games., , and . AAMAS, page 27-36. ACM, (2015)