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Hierarchy Through Composition with Multitask LMDPs., , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 3017-3026. PMLR, (2017)Anticipatory Bayesian Policy Selection for Online Adaptation of Collaborative Robots to Unknown Human Types., , and . AAMAS, page 77-85. International Foundation for Autonomous Agents and Multiagent Systems, (2019)Hierarchical Subtask Discovery with Non-Negative Matrix Factorization., , and . ICLR (Poster), OpenReview.net, (2018)A Bayesian Approach for Learning and Tracking Switching, Non-Stationary Opponents: (Extended Abstract)., , , , and . AAMAS, page 1315-1316. ACM, (2016)Hierarchical Subtask Discovery With Non-Negative Matrix Factorization., , and . CoRR, (2017)Reasoning about Unforeseen Possibilities During Policy Learning., , , , and . CoRR, (2018)Generating Interpretable Play-Style Descriptions Through Deep Unsupervised Clustering of Trajectories., , , and . IEEE Trans. Games, 15 (4): 507-516 (December 2023)ROSARL: Reward-Only Safe Reinforcement Learning., , , , and . CoRR, (2023)Learning to Follow Language Instructions with Compositional Policies., , , , , and . CoRR, (2021)Generalisable Agents for Neural Network Optimisation., , , , , , , and . CoRR, (2023)