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Semantics and algorithms for trustworthy commitment achievement under model uncertainty., , and . Auton. Agents Multi Agent Syst., 34 (1): 19 (2020)Near-optimal reinforcement learning in polynomial time, and . Proc. 15th International Conf. on Machine Learning, page 260--268. Morgan Kaufmann, San Francisco, CA, (1998)Between MDPs and Semi-MDPs: learning, planning, and representing knowledge at multiple temporal scales, , and . 98-74. University of Massachusetts, Amherst, MA 01003, (1998)Risk-aware analysis for interpretations of probabilistic achievement and maintenance commitments., , and . Artif. Intell., (April 2023)Object-oriented state editing for HRL., , , , , , and . CoRR, (2019)Bootstrapped Meta-Learning., , , , , and . CoRR, (2021)Disentangled Cumulants Help Successor Representations Transfer to New Tasks., , , , , , , and . CoRR, (2019)Discovering Diverse Nearly Optimal Policies withSuccessor Features., , , , , and . CoRR, (2021)Reinforcement Learning of Implicit and Explicit Control Flow in Instructions., , , and . CoRR, (2021)In-context Reinforcement Learning with Algorithm Distillation., , , , , , , , , and 4 other author(s). CoRR, (2022)