Richard S Sutton and Andrew G Barto. Reinforcement learning: An introduction. MIT press, 2018.
Leslie Pack Kaelbling, Michael L Littman, and Anthony R Cassandra. Planning and acting in partially observable stochastic domains. Artificial intelligence, 101(1-2):99-134, 1998. Publisher: Elsevier.
Tianwei Ni, Benjamin Eysenbach, and Ruslan Salakhutdinov. Recurrent Model-Free RL Can Be a Strong Baseline for Many POMDPs. In Proceedings of the 39th International Conference on Machine Learning, pages 16691-16723. PMLR, June 2022. URL https://proceedings.mlr. press/v162/ni22a.html. ISSN: 2640-3498.
Zhihan Yang and Hai Nguyen. Recurrent Off-policy Baselines for Memory-based Continuous Control. Deep RL Workshop, NeurIPS 2021, 2021.
Matthew Hausknecht and Peter Stone. Deep recurrent q-learning for partially observable mdps. In 2015 aaai fall symposium series, 2015.
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 Hal Daumé III and Aarti Singh, editors, Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 7487-7498. PMLR, July 2020. URL https://proceedings.mlr.press/v119/parisotto20a.htm1.
Steven Morad, Ryan Kortvelesy, Matteo Bettini, Stephan Liwicki, and Amanda Prorok. POPGym: Benchmarking Partially Observable Reinforcement Learning. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=chDrutUTs0K.
Danijar Hafner, Jurgis Pasukonis, Jimmy Ba, and Timothy Lillicrap. Mastering Diverse Domains through World Models. arXiv preprint arXiv:2301.04104, 2023.
Steven Kaptuowski, Víctor Campos, Ray Jiang, Nemanja Rakicevic, Hado van Hasselt, Charles Blundell, and Adria Puigdomenech Badia. Human-level atari 200x faster. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=JtC6yOHRoJJ.
John Schulman, Sergey Levine, Pieter Abbeel, Michael Jordan, and Philipp Moritz. Trust region policy optimization. In International conference on machine learning, pages 1889-1897. PMLR, 2015.
Chiyuan Zhang, Oriol Vinyals, Remi Munos, and Samy Bengio. A Study on Overfitting in Deep Reinforcement Learning. arXiv, 2018. URL https://arxiv.org/pdf/1804.06893.pdf.
Albert Gu, Isys Johnson, Karan Goel, Khaled Saab, Tri Dao, Atri Rudra, and Christopher Ré. Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers. In Advances in Neural Information Processing Systems, volume 34, pages 572-585. Curran Associates, Inc., 2021. URL https://proceedings.neurips.cc/paper/2021/bit/05546b0e38ab9175cd905eebcc6ebb76-AAbstract.html.
Aaron Voelker, Ivana Kajic, and Chris Eliasmith. Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. URL https://papers.nips.cc/paper/2019/bitstream/952285b9b7e7a1be5aa7849f32fff05-Abstract.html.
Sepp Hochreiter and Jürgen Schmidhuber. Long Short-Term Memory. Neural Computation, 9 (8):1735-1780, November 1997. ISSN 0899-7667. doi: 10.1162/neco.1997.9.8.1735. URL https://doi.org/10.1162/neco.1997.9.8.1735.
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Technical Report arXiv:1412.3555, arXiv, December 2014. URL http://arxiv.org/abs/1412.3555.arXiv:1412.3555 [cs] type: article.