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Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients.

, , , , , , , , , , and . AAMAS, page 492-501. International Foundation for Autonomous Agents and Multiagent Systems, (2020)

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Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients., , , , , , , , , and 1 other author(s). AAMAS, page 492-501. International Foundation for Autonomous Agents and Multiagent Systems, (2020)The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning., , , , , and . ICLR (Poster), OpenReview.net, (2018)The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning, , , , , and . ICLR, (2017)cite arxiv:1704.04651.A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning, , , , , , , and . (2017)cite arxiv:1711.00832Comment: Camera-ready copy of NIPS 2017 paper, including appendix.Deep Q-learning From Demonstrations., , , , , , , , , and 4 other author(s). AAAI, page 3223-3230. AAAI Press, (2018)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)Quantile Credit Assignment., , , , , , , , , and 3 other author(s). ICML, volume 202 of Proceedings of Machine Learning Research, page 24517-24531. PMLR, (2023)