Michael L. Littman, Thomas L. Dean, and Leslie Pack Kaelbling. On the complexity of solving markov decision problems. In UAI '95: Proceedings of the Eleventh Annual Conference on Uncertainty in Artificial Intelligence, Montreal, Quebec, Canada, August 18-20, 1995, pages 394-402. Morgan Kaufmann, 1995.
Marlos C. Machado, Marc G. Bellemare, and Michael Bowling. A laplacian framework for option discovery in reinforcement learning. In Proceedings of the 34th International Conference on Machine Learning - Volume 70, page 2295-2304, 2017.
Marlos C Machado, Marc G Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, and Michael Bowling. Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents. Journal of Artificial Intelligence Research, 61:523-562, 2018.
Priyanka Mandikal and Kristen Grauman. Learning dexterous grasping with object-centric visual affordances. In 2021 IEEE international conference on robotics and automation (ICRA), pages 6169-6176. IEEE, 2021.
Priyanka Mandikal and Kristen Grauman. Dexvip: Learning dexterous grasping with human hand pose priors from video. In Conference on Robot Learning, pages 651-661. PMLR, 2022.
Roberto Martin-Martin, Michelle A Lee, Rachel Gardner, Silvio Savarese, Jeannette Bohg, and Animesh Garg. Variable impedance control in end-effector space: An action space for reinforcement learning in contact-rich tasks. In 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS), pages 1010-1017. IEEE, 2019.
Elizabeth Amy Mcgovern. Autonomous discovery of temporal abstractions from interaction with an environment. PhD thesis, University of Massachusetts at Amherst, 2002.
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529, 2015.
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari. Learning with noisy labels. Advances in neural information processing systems, 26, 2013.
Andrei Nica, Khimya Khetarpal, and Doina Precup. The paradox of choice: Using attention in hierarchical reinforcement learning. arXiv preprint arXiv:2201.09653, 2022.
Doina Precup. Temporal abstraction in reinforcement learning. University of Massachusetts Amherst, 2000.
Doina Precup, Richard S Sutton, and Satinder Singh. Theoretical results on reinforcement learning with temporally abstract options. In Machine Learning: ECML-98: 10th European Conference on Machine Learning Chemnitz, Germany, April 21-23, 1998 Proceedings 10, pages 382-393. Springer, 1998.
Jette Randlov and Preben Alstrom. Learning to drive a bicycle using reinforcement learning and shaping. In Proceedings of the 15th International Conference on Machine Learning, volume 98, pages 463-471, 1998.
Mark B. Ring. *Continual learning in reinforcement environments*. PhD thesis, University of Texas at Austin, TX, USA, 1995.
Eric Rosen, Ben M Abbatematteo, Skye Thompson, Tuluhan Akbulut, and George Konidaris. On the role of structure in manipulation skill learning. In CoRL 2022 Workshop on Learning, Perception, and Abstraction for Long-Horizon Planning, 2022.
Erol Şahin, Maya Cakmak, Mehmet R Doğar, Emre Üğur, and Göktürk Üçoluk. To afford or not to afford: A new formalization of affordances toward affordance-based robot control. Adaptive Behavior, 15(4):447-472, 2007.
Tom Schaul and Mark B. Ring. Better generalization with forecasts. In Francesca Rossi, editor, *IJCAI* 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3-9, 2013, pages 1656-1662, 2013.