[36] Cristiano Varin, Nancy Reid, and David Firth. An overview of composite likelihood methods. Statistica Sinica, pages 5-42, 2011.
[37] Faraz Torabi, Garrett Warnell, and Peter Stone. Behavioral cloning from observation. arXiv preprint arXiv:1805.01954, 2018.
[38] Zhuangdi Zhu, Kaixiang Lin, Bo Dai, and Jiayu Zhou. Off-policy imitation learning from observations. Advances in Neural Information Processing Systems, 33:12402-12413, 2020.
[39] Emanuel Todorov. General duality between optimal control and estimation. In 2008 47th IEEE Conference on Decision and Control, pages 4286-4292. IEEE, 2008.
[40] Marc Toussaint. Robot trajectory optimization using approximate inference. In Proceedings of the 26th annual international conference on machine learning, pages 1049-1056, 2009.
[41] Hilbert J Kappen, Vicenç Gómez, and Manfred Opper. Optimal control as a graphical model inference problem. Machine learning, 87:159-182, 2012.
[42] Richard S Sutton. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Machine learning proceedings 1990, pages 216-224. Elsevier, 1990.
[43] Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
[44] Jonathan Ferrer-Mestres, Thomas G Dietterich, Olivier Buffet, and Iadine Chades. Solving k-mdps. In Proceedings of the International Conference on Automated Planning and Scheduling, volume 30, pages 110-118, 2020.
[45] John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347, 2017.
[46] Stéphane Ross and Drew Bagnell. Efficient reductions for imitation learning. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 661-668. JMLR Workshop and Conference Proceedings, 2010.
[47] Ramesh Amit and Maja Matari. Learning movement sequences from demonstration. In Proceedings 2nd International Conference on Development and Learning. ICDL 2002, pages 203-208. IEEE, 2002.
[48] Christopher G Atkeson and Stefan Schaal. Robot learning from demonstration. In ICML, volume 97, pages 12-20. CiteSeer, 1997.
[49] Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning. A survey of robot learning from demonstration. Robotics and autonomous systems, 57(5):469-483, 2009.
[50] Pieter Abbeel and Andrew Y Ng. Apprenticeship learning via inverse reinforcement learning. In Proceedings of the 21st international conference on Machine learning, page 1, 2004.
[51] Nathan D Ratliff, J Andrew Bagnell, and Martin A Zinkevich. Maximum margin planning. In Proceedings of the 23rd international conference on Machine learning, pages 729-736, 2006.
[52] Rahul Kidambi, Jonathan Chang, and Wen Sun. Mobile: Model-based imitation learning from observation alone. arXiv preprint arXiv:2102.10769, 2021.
[53] YuXuan Liu, Abhishek Gupta, Pieter Abbeel, and Sergey Levine. Imitation from observation: Learning to imitate behaviors from raw video via context translation. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 1118-1125. IEEE, 2018.
[54] Song Chun Zhu, Yingnian Wu, and David Mumford. Filters, random fields and maximum entropy (frame): Towards a unified theory for texture modeling. International Journal of Computer Vision, 27(2):107-126, 1998.
[55] Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and Fujie Huang. A tutorial on energy-based learning. Predicting structured data, 1(0), 2006.