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Multi-objectivization of reinforcement learning problems by reward shaping., , , , , и . IJCNN, стр. 2315-2322. IEEE, (2014)An Exploration Strategy Facing Non-Stationary Agents., , , , и . AAMAS, стр. 922-923. ACM, (2017)Speeding up Tabular Reinforcement Learning Using State-Action Similarities., , и . AAMAS, стр. 1722-1724. ACM, (2017)Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning., и . CoRR, (2017)An Introduction to Intertask Transfer for Reinforcement Learning., и . AI Magazine, 32 (1): 15-34 (2011)Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL., , и . CoRR, (2018)Leveraging human knowledge in tabular reinforcement learning: a study of human subjects., , , и . Knowledge Eng. Review, (2018)Is multiagent deep reinforcement learning the answer or the question? A brief survey., , и . CoRR, (2018)Transfer via inter-task mappings in policy search reinforcement learning., , и . AAMAS, стр. 37. IFAAMAS, (2007)Abstraction and Generalization in Reinforcement Learning: A Summary and Framework., , и . ALA, том 5924 из Lecture Notes in Computer Science, стр. 1-32. Springer, (2009)