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An Exploration Strategy Facing Non-Stationary Agents., , , , and . AAMAS, page 922-923. ACM, (2017)Speeding up Tabular Reinforcement Learning Using State-Action Similarities., , and . AAMAS, page 1722-1724. ACM, (2017)Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning., and . CoRR, (2017)Multi-objectivization of reinforcement learning problems by reward shaping., , , , , and . IJCNN, page 2315-2322. IEEE, (2014)Leveraging human knowledge in tabular reinforcement learning: a study of human subjects., , , and . Knowledge Eng. Review, (2018)Is multiagent deep reinforcement learning the answer or the question? A brief survey., , and . CoRR, (2018)When should there be a "Me" in "Team"?: distributed multi-agent optimization under uncertainty., , , , and . AAMAS, page 109-116. IFAAMAS, (2010)Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL., , and . CoRR, (2018)Transfer via inter-task mappings in policy search reinforcement learning., , and . AAMAS, page 37. IFAAMAS, (2007)Protecting against evaluation overfitting in empirical reinforcement learning., , , and . ADPRL, page 120-127. IEEE, (2011)