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Learning potential functions and their representations for multi-task reinforcement learning.

, and . Auton. Agents Multi Agent Syst., 28 (4): 637-681 (2014)

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Traffic Light Control by Multiagent Reinforcement Learning Systems., , , and . Interactive Collaborative Information Systems, volume 281 of Studies in Computational Intelligence, Springer, (2010)CAML: Fast Context Adaptation via Meta-Learning., , , , and . CoRR, (2018)Multi-Agent Common Knowledge Reinforcement Learning., , , , , and . CoRR, (2018)Stable Opponent Shaping in Differentiable Games., , , , and . CoRR, (2018)QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning., , , , , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 4292-4301. PMLR, (2018)Comparing evolutionary and temporal difference methods in a reinforcement learning domain., , and . GECCO, page 1321-1328. ACM, (2006)Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning., , , , , and . J. Mach. Learn. Res., (2020)A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs., , , , , and . ICML, volume 97 of Proceedings of Machine Learning Research, page 4343-4351. PMLR, (2019)VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning., , , , , , and . ICLR, OpenReview.net, (2020)Facial feedback for reinforcement learning: a case study and offline analysis using the TAMER framework., , , and . Auton. Agents Multi Agent Syst., 34 (1): 22 (2020)