[90] Gokul Swamy, Nived Rajaraman, Matt Peng, Sanjiban Choudhury, J Bagnell, Steven Z Wu, Jiantao Jiao, and Kannan Ramchandran. Minimax optimal online imitation learning via replay estimation. Advances in Neural Information Processing Systems, 2022.
[91] Fumihiro Sasaki and Ryota Yamashina. Behavioral cloning from noisy demonstrations. In International Conference on Learning Representations, 2021.
[92] Deepak Ramachandran and Eyal Amir. Bayesian inverse reinforcement learning. In International Joint Conference on Artificial Intelligence, 2007.
[93] Jaedeug Choi and Kee-Eung Kim. MAP inference for Bayesian inverse reinforcement learning. Advances in Neural Information Processing Systems, 2011.
[94] Giuseppe Da Prato and Jerzy Zabczyk. Stochastic equations in infinite dimensions. Cambridge University Press, 2014.
[95] Chris Chatfield. The analysis of time series: An introduction. Chapman and Hall, 1989.
[96] Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax Weiss, and Balaji Lakshminarayanan. Simple and principled uncertainty estimation with deterministic deep learning via distance awareness. In Advances in Neural Information Processing Systems, 2020.
[97] John Schulman. Approximating KL divergence. http://joschu.net/blog/kl-approx.html, 2020.
[98] Shengyang Sun, Guodong Zhang, Jiaxin Shi, and Roger Grosse. Functional variational Bayesian neural networks. In International Conference on Learning Representations, 2019.
[99] Joe Watson, Jihao Andreas Lin, Pascal Klink, and Jan Peters. Neural linear models with functional Gaussian process priors. In Third Symposium on Advances in Approximate Bayesian Inference, 2021.
[100] Div99. IQ-Learn implementation. https://github.com/Div99/IQ-Learn, 2021.
[101] Yihong Wu. Lecture notes for ECE598YW: Information-theoretic methods in high-dimensional statistics. Yale University, 2016.
[102] James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang. JAX: Composable transformations of Python+NumPy programs, 2018. URL http://github.com/google/jax.
[103] Igor Babuschkin, Kate Baumli, Alison Bell, Surya Bhupatiraju, Jake Bruce, Peter Buchovsky, David Budden, Trevor Cai, Aidan Clark, Ivo Danihelka, Antoine Dedieu, Claudio Fantacci, Jonathan Godwin, Chris Jones, Ross Hemsley, Tom Hennigan, Matteo Hessel, Shaobo Hou, Steven Kapturowski, Thomas Keck, Iurii Kemaev, Michael King, Markus Kunesch, Lena Martens, Hamza Merzic, Vladimir Mikulik, Tamara Norman, George Papamakarios, John Quan, Roman Ring, Francisco Ruiz, Alvaro Sanchez, Laurent Sartran, Rosalia Schneider, Eren Sezener, Stephen Spencer, Srivatsan Srinivasan, Miloš Stanojevic, Wojciech Stokowiec, Luyu Wang, Guangyao Zhou, and Fabio Viola. The DeepMind JAX Ecosystem, 2020. URL http://github.com/deepmind.
[104] lviano. Proximal point imitation learning implementation. https://github.com/lviano/p2il, 2022.
[105] geon-hyeong. DemoDICE implementation. https://github.com/geon-hyeong/imitation-dice, 2022.
[106] JasonMa2016. SMODICE implementation. https://github.com/JasonMa2016/SMODICE, 2022.
[107] Lasse Espeholt, Hubert Soyer, Remi Munos, Karen Simonyan, Vlad Mnih, Tom Ward, Yotam Doron, Vlad Firoiu, Tim Harley, Iain Dunning, et al. Impala: Scalable distributed deep-RL with importance weighted actor-learner architectures. In International conference on machine learning, 2018.