Ernst, D., Geurts, P., and Wehenkel, L. (2005). Tree-based batch mode reinforcement learning. Journal of Machine Learning Research, 6.
Farama Foundation (2023). Farama-Foundation/Gymnasium. original-date: 2022-09-08T01:58:05Z.
Fisher, R. A. and Tippett, L. H. C. (1928). Limiting forms of the frequency distribution of the largest or smallest member of a sample. In Mathematical proceedings of the Cambridge philosophical society, volume 24, pages 180-190. Cambridge University Press.
Fortunato, M., Azar, M. G., Piot, B., Menick, J., Osband, I., Graves, A., Mnih, V., Munos, R., Hassabis, D., Pietquin, O., et al. (2017). Noisy networks for exploration. arXiv preprint arXiv:1706.10295.
Fox, R., Pakman, A., and Tishby, N. (2015). Taming the noise in reinforcement learning via soft updates. arXiv preprint arXiv:1512.08562.
Fujimoto, S., Hoof, H., and Meger, D. (2018). Addressing function approximation error in actor-critic methods. In International Conference on Machine Learning, pages 1587-1596. PMLR.
Garg, D., Hejna, J., Geist, M., and Ermon, S. (2023). Extreme q-learning: Maxent rl without entropy. arXiv preprint arXiv:2301.02328.
Glorot, X., Bordes, A., and Bengio, Y. (2011). Deep sparse rectifier neural networks. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pages 315-323. JMLR Workshop and Conference Proceedings.
Gnedenko, B. (1943). Sur la distribution limite du terme maximum d'une série aleatoire. Annals of mathematics, pages 423-453.
Gumbel, E. J. (1935). Les valeurs extrêmes des distributions statistiques. In Annales de l'institut Henri Poincaré, volume 5, pages 115-158.
Haarnoja, T., Tang, H., Abbeel, P., and Levine, S. (2017). Reinforcement learning with deep energy-based policies. In International conference on machine learning, pages 1352-1361. PMLR.
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S. (2018a). Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor. In International conference on machine learning, pages 1861-1870. PMLR.
Haarnoja, T., Zhou, A., Hartikainen, K., Tucker, G., Ha, S., Tan, J., Kumar, V., Zhu, H., Gupta, A., Abbeel, P., et al. (2018b). Soft actor-critic algorithms and applications. arXiv preprint arXiv:1812.05905.
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D. (2018). Deep reinforcement learning that matters. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 32.
Hennigan, T., Cai, T., Norman, T., and Babuschkin, I. (2020). Haiku: Sonnet for JAX.
Hessel, M., Modayil, J., Van Hasselt, H., Schaul, T., Ostrovski, G., Dabney, W., Horgan, D., Piot, B., Azar, M., and Silver, D. (2018). Rainbow: Combining improvements in deep reinforcement learning. In Thirty-second AAAI conference on artificial intelligence.
Hinton, G., Srivastava, N., and Swersky, K. (2012). Neural networks for machine learning lecture 6a overview of mini-batch gradient descent. Cited on, 14(8):2.
Hoffman, M. W., Shahriari, B., Aslanides, J., Barth-Maron, G., Momchev, N., Sinopalnikov, D., Stańczyk, P., Ramos, S., Raichuk, A., Vincent, D., Hussenot, L., Dadashi, R., Dulac-Arnold, G., Orsini, M., Jacq, A., Ferret, J., Vieillard, N., Ghasemipour, S. K. S., Girgin, S., Pietquin, O., Behbahani, F., Norman, T., Abdolmaleki, A., Cassirer, A., Yang, F., Baumli, K., Henderson, S., Friesen, A., Haroun, R., Novikov, A., Colmenarejo, S. G., Cabi, S., Gulcehre, C., Paine, T. L., Srinivasan, S., Cowie, A., Wang, Z., Piot, B., and de Freitas, N. (2020). Acme: A research framework for distributed reinforcement learning. arXiv preprint arXiv:2006.00979.
Howard, R. A. (1960). Dynamic programming and markov processes. John Wiley.
Huang, S., Dossa, R. F. J., Raffin, A., Kanervisto, A., and Wang, W. (2022a). The 37 implementation details of proximal policy optimization. In ICLR Blog Track. https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/.
Huang, S., Dossa, R. F. J., Ye, C., Braga, J., Chakraborty, D., Mehta, K., and Araujo, J. G. (2022b). Cleanrl: High-quality single-file implementations of deep reinforcement learning algorithms. Journal of Machine Learning Research, 23(274):1-18.