Pierre-Luc Bacon, Jean Harb, and Doina Precup. The option-critic architecture. In Thirty-First AAAI Conference on Artificial Intelligence, 2017.
Akhil Bagaria and George Konidaris. Option discovery using deep skill chaining. In International Conference on Learning Representations, 2020. URL https://openreview.net/forum?id=B1gqipNYwH.
Akhil Bagaria, Jason Senthil, Matthew Slivinski, and George Konidaris. Robustly learning composable options in deep reinforcement learning. In 30th International Joint Conference on Artificial Intelligence, 2021a.
Akhil Bagaria, Jason K Senthil, and George Konidaris. Skill discovery for exploration and planning using deep skill graphs. In International Conference on Machine Learning, pages 521-531. PMLR, 2021b.
Adrien Baranes and Pierre-Yves Oudeyer. Active learning of inverse models with intrinsically motivated goal exploration in robots. Robotics and Autonomous Systems, 61(1):49-73, 2013.
Andre Barreto, Diana Borsa, Shaobo Hou, Gheorghe Comanici, Eser Aygün, Philippe Hamel, Daniel Toyama, Shibl Mourad, David Silver, Doina Precup, et al. The option keyboard: Combining skills in reinforcement learning. In Advances in Neural Information Processing Systems, pages 13031-13041, 2019.
Andrew G Barto and Sridhar Mahadevan. Recent advances in hierarchical reinforcement learning. Discrete event dynamic systems, 13(1-2):41-77, 2003.
Marc G Bellemare, Yavar Naddaf, Joel Veness, and Michael Bowling. The arcade learning environment: An evaluation platform for general agents. Journal of Artificial Intelligence Research, 47: 253-279, 2013.
Christopher M Bishop and Nasser M Nasrabadi. Pattern recognition and machine learning, volume 4. Springer, 2006.
Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, et al. Do as I can, not as I say: Grounding language in robotic affordances. In Conference on Robot Learning, pages 287-318. PMLR, 2023.
Maxime Chevalier-Boisvert, Lucas Willems, and Suman Pal. Minimalistic gridworld environment for gymnasium, 2018. URL https://github.com/Farama-Foundation/Minigrid.
Peter Dayan and Geoffrey E Hinton. Feudal reinforcement learning. In Advances in neural information processing systems, pages 271-278, 1993.
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine. Diversity is all you need: Learning skills without a reward function. In International Conference on Learning Representations, 2019. URL https://openreview.net/forum?id=SJx63jRqFm.
Kuan Fang, Yuke Zhu, Animesh Garg, Andrey Kurenkov, Viraj Mehta, Li Fei-Fei, and Silvio Savarese. Learning task-oriented grasping for tool manipulation from simulated self-supervision. The International Journal of Robotics Research, 39(2-3):202-216, 2020.
Richard E Fikes, Peter E Hart, and Nils J Nilsson. Learning and executing generalized robot plans. Artificial intelligence, 3:251-288, 1972.
Justin Fu, Aviral Kumar, Ofir Nachum, George Tucker, and Sergey Levine. D4RL: Datasets for deep data-driven reinforcement learning. arXiv preprint arXiv:2004.07219, 2020.
Scott Fujimoto, Herke Hoof, and David Meger. Addressing function approximation error in actor-critic methods. In Proceedings of the 35th International Conference on Machine Learning, pages 1582-1591, 2018.
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomas Lozano-Perez. Integrated task and motion planning. Annual review of control, robotics, and autonomous systems, 4:265-293, 2021.