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How a General-Purpose Commonsense Ontology can Improve Performance of Learning-Based Image Retrieval.

, , , and . IJCAI, page 1283-1289. ijcai.org, (2017)

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Learning Symbolic Representations for Reinforcement Learning of Non-Markovian Behavior., , , and . CoRR, (2023)Reward Machines.. University of Toronto, Canada, (2022)LTL2Action: Generalizing LTL Instructions for Multi-Task RL., , , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 10497-10508. PMLR, (2021)Real-Time Heuristic Search with LTLf Goals., , and . IJCAI, page 4785-4792. ijcai.org, (2022)Be Considerate: Avoiding Negative Side Effects in Reinforcement Learning., , , and . AAMAS, page 18-26. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)Training Binarized Neural Networks Using MIP and CP., , , , , and . CP, volume 11802 of Lecture Notes in Computer Science, page 401-417. Springer, (2019)Learning reward machines: A study in partially observable reinforcement learning., , , , , and . Artif. Intell., (October 2023)Be Considerate: Objectives, Side Effects, and Deciding How to Act., , , and . CoRR, (2021)Learning Reward Machines for Partially Observable Reinforcement Learning., , , , , and . NeurIPS, page 15497-15508. (2019)Learning Belief Representations for Partially Observable Deep RL., , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 35970-35988. PMLR, (2023)