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Between MDPs and Semi-MDPs: learning, planning, and representing knowledge at multiple temporal scales, , and . 98-74. University of Massachusetts, Amherst, MA 01003, (1998)Near-optimal reinforcement learning in polynomial time, and . Proc. 15th International Conf. on Machine Learning, page 260--268. Morgan Kaufmann, San Francisco, CA, (1998)Semantics and algorithms for trustworthy commitment achievement under model uncertainty., , and . Auton. Agents Multi Agent Syst., 34 (1): 19 (2020)Risk-aware analysis for interpretations of probabilistic achievement and maintenance commitments., , and . Artif. Intell., (April 2023)Blockchain and Deep Learning for Secure Communication in Digital Twin Empowered Industrial IoT Network., , , , , and . IEEE Trans. Netw. Sci. Eng., 10 (5): 2802-2813 (September 2023)Multi-task seizure detection: addressing intra-patient variation in seizure morphologies., , , , and . Mach. Learn., 102 (3): 309-321 (2016)Object-oriented state editing for HRL., , , , , , and . CoRR, (2019)Discovering Diverse Nearly Optimal Policies withSuccessor Features., , , , , and . CoRR, (2021)Bootstrapped Meta-Learning., , , , , and . CoRR, (2021)Disentangled Cumulants Help Successor Representations Transfer to New Tasks., , , , , , , and . CoRR, (2019)