[78] Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Misha Laskin, Pieter Abbeel, Aravind Srinivas, and Igor Mordatch. Decision Transformer: Reinforcement Learning via Sequence Modeling. In Advances in Neural Information Processing Systems, volume 34, pages 15084-15097. Curran Associates, Inc., 2021.
[79] Michael Janner, Qiyang Li, and Sergey Levine. Offline Reinforcement Learning as One Big Sequence Modeling Problem. In Advances in Neural Information Processing Systems, volume 34, pages 1273-1286. Curran Associates, Inc., 2021.
[80] Richard S. Sutton. Learning to predict by the methods of temporal differences. Machine Learning, 3(1):9-44, August 1988.
[81] Hado van Hasselt, Sephora Madjiheurem, Matteo Hessel, David Silver, André Barreto, and Diana Borsa. Expected Eligibility Traces. In Association for the Advancement of Artificial Intelligence. arXiv, February 2021. arXiv:2007.01839 [cs, stat].
[82] 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, Rosalia Schneider, Eren Sezener, Stephen Spencer, Srivatsan Srinivasan, Wojciech Stokowiec, Luyu Wang, Guangyao Zhou, and Fabio Viola. The DeepMind JAX Ecosystem, 2020.
[83] Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. Deep Variational Information Bottleneck. 2017.
[84] Naftali Tishby, Fernando C. Pereira, and William Bialek. The information bottleneck method, April 2000. arXiv:physics/0004057.
[85] Matthew Hausknecht and Peter Stone. Deep Recurrent Q-Learning for Partially Observable MDPs. In Association for the Advancement of Artificial Intelligence. arXiv, 2015. arXiv:1507.06527 [cs].
[86] Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, and James Davidson. Learning Latent Dynamics for Planning from Pixels. In Proceedings of the 36th International Conference on Machine Learning, pages 2555–2565. PMLR, May 2019. ISSN: 2640-3498.
[87] Karol Gregor, Danilo Jimenez Rezende, Frederic Besse, Yan Wu, Hamza Merzic, and Aaron van den Oord. Shaping Belief States with Generative Environment Models for RL. In 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). arXiv, June 2019. Number: arXiv:1906.09237 arXiv:1906.09237 [cs, stat].
[88] Karol Gregor, George Papamakarios, Frederic Besse, Lars Buesing, and Theophane Weber. Temporal Difference Variational Auto-Encoder. 2019.
[89] Matthijs T J Span. Partially Observable Markov Decision Processes. Reinforcement Learning, page 27.
[90] K. J. Astrom. Optimal Control of Markov decision processes with incomplete state estimation. J. Math. Anal. Applic., 10:174-205, 1965.
[91] Edward C. Tolman. Cognitive maps in rats and men. Psychological Review, 55:189-208, 1948.  
Place: US Publisher: American Psychological Association.
[92] Alexander A. Alemi, Ben Poole, Ian Fischer, Joshua V. Dillon, Rif A. Saurous, and Kevin Murphy. Fixing a Broken ELBO. In Proceedings of the 35th International Conference on Machine Learning., arXiv, February 2018. arXiv:1711.00464 [cs, stat].
[93] Irina Higgins, Loic Matthew, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. July 2022.