[35] Arnav Kumar Jain, Shivakanth Sujit, Shruti Joshi, Vincent Michalski, Danijar Hafner, and Samira Ebrahimi Kahou. Learning robust dynamics through variational sparse gating. In Advances in Neural Information Processing Systems, 2022.
[36] David M Knigge, David W Romero, Albert Gu, Efstratios Gavves, Erik J Bekkers, Jakub Mikolaj Tomczak, Mark Hoogendoorn, and Jan-jakob Sonke. Modelling long range dependencies in ND: From task-specific to a general purpose CNN. In International Conference on Learning Representations, 2023.
[37] Andrew Kyle Lampinen, Stephanie C.Y. Chan, Andrea Banino, and Felix Hill. Towards mental time travel: a hierarchical memory for reinforcement learning agents. In Advances in Neural Information Processing Systems, 2021.
[38] Chris Lu, Yannick Schroecker, Albert Gu, Emilio Parisotto, Jakob Foerster, Satinder Singh, and Feryal Behbahani. Structured state space models for in-context reinforcement learning. arXiv preprint arXiv:2303.03982, 2023.
[39] Marcelo G Mattar and Máté Lengyel. Planning in the brain. Neuron, 110(6):914–934, 2022.
[40] Harsh Mehta, Ankit Gupta, Ashok Cutkosky, and Behnam Neyshabur. Long range language modeling via gated state spaces. In International Conference on Learning Representations, 2023.
[41] Vincent Micheli, Eloi Alonso, and François Fleuret. Transformers are sample-efficient world models. In International Conference on Learning Representations, 2023.
[42] Thomas M Moerland, Joost Broekens, Aske Plaat, and Catholijn M Jonker. Model-based reinforcement learning: A survey. Foundations and Trends® in Machine Learning, 16(1):1-118, 2023.
[43] Eric Nguyen, Karan Goel, Albert Gu, Gordon Downs, Preey Shah, Tri Dao, Stephen Baccus, and Christopher Ré. S4ND: Modeling images and videos as multidimensional signals with state spaces. In Advances in Neural Information Processing Systems, 2022.
[44] Tung D Nguyen, Rui Shu, Tuan Pham, Hung Bui, and Stefano Ermon. Temporal predictive coding for model-based planning in latent space. In International Conference on Machine Learning, 2021.
[45] Masashi Okada and Tadahiro Taniguchi. Dreaming: Model-based reinforcement learning by latent imagination without reconstruction. In ICRA, 2021.
[46] Masashi Okada and Tadahiro Taniguchi. DreamingV2: Reinforcement learning with discrete world models without reconstruction. In IROS, 2022.
[47] Antonio Orvieto, Samuel L Smith, Albert Gu, Anushan Fernando, Caglar Gulcehre, Razvan Pascanu, and Soham De. Resurrecting recurrent neural networks for long sequences. In International Conference on Machine Learning, 2023.
[48] Emilio Parisotto, Francis Song, Jack Rae, Razvan Pascanu, Caglar Gulcehre, Siddhant Jayakumar, Max Jaderberg, Raphaël Lopez Kaufman, Aidan Clark, Seb Noury, Matthew Botvinick, Nicolas Heess, and Raia Hadsell. Stabilizing Transformers for reinforcement learning. In International Conference on Machine Learning, 2020.
[49] Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. On the difficulty of training recurrent neural networks. In International Conference on Machine Learning, 2013.
[50] Jurgis Pašukonis, Timothy Lillicrap, and Danijar Hafner. Evaluating long-term memory in 3D mazes. In International Conference on Learning Representations, 2023.
[51] Joel Pearson. The human imagination: the cognitive neuroscience of visual mental imagery. Nature Reviews Neuroscience, 20(10):624-634, 2019.
[52] Marco Pleines, Matthias Pallasch, Frank Zimmer, and Mike Preuss. Memory Gym: Partially observable challenges to memory-based agents. In International Conference on Learning Representations, 2023.