Huber, P. J. (1992). Robust estimation of a location parameter. In *Breakthroughs in statistics: Methodology and distribution*, pages 492-518. Springer.
Irpan, A. (2018). Deep reinforcement learning doesn't work yet. https://www alexirpan.com/2018/02/14/rl-hard.html.
Jowitt, P. (1979). The extreme-value type-1 distribution and the principle of maximum entropy. Journal of Hydrology, 42(1-2):23-38.
Karimpanal, T. G., Le, H., Abdolshah, M., Rana, S., Gupta, S., Tran, T., and Venkatesh, S. (2021). Balanced q-learning: Combining the influence of optimistic and pessimistic targets. arXiv preprint arXiv:2111.02787.
Kendall, A. and Gal, Y. (2017). What uncertainties do we need in bayesian deep learning for computer vision? Advances in neural information processing systems, 30.
Kimball, B. F. (1946). Sufficient statistical estimation functions for the parameters of the distribution of maximum values. The Annals of Mathematical Statistics, 17(3):299-309.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kostrikov, I. (2021). JAXRL: Implementations of Reinforcement Learning algorithms in JAX.
Kuznetsov, A., Grishin, A., Tsypin, A., Ashukha, A., Kadurin, A., and Vetrov, D. (2022). Automating control of overestimation bias for reinforcement learning.
Kuznetsov, A., Shvechikov, P., Grishin, A., and Vetrov, D. (2020). Controlling overestimation bias with truncated mixture of continuous distributional quantile critics. In International Conference on Machine Learning, pages 5556-5566. PMLR.
La, P. and Ghavamzadeh, M. (2013). Actor-critic algorithms for risk-sensitive mdps. Advances in neural information processing systems, 26.
Lacoste, A., Luccioni, A., Schmidt, V., and Dandres, T. (2019). Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700.
Lan, Q., Pan, Y., Fyshe, A., and White, M. (2020). Maxmin q-learning: Controlling the estimation bias of q-learning. arXiv preprint arXiv:2002.06487.
Laskin, M., Yarats, D., Liu, H., Lee, K., Zhan, A., Lu, K., Cang, C., Pinto, L., and Abbeel, P. (2021). Urlb: Unsupervised reinforcement learning benchmark. In Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2).
Lee, D. and Powell, W. (2012). An intelligent battery controller using bias-corrected q-learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 26, pages 316-322.
Lee, K., Laskin, M., Srinivas, A., and Abbeel, P. (2021). Sunrise: A simple unified framework for ensemble learning in deep reinforcement learning. In International Conference on Machine Learning, pages 6131-6141. PMLR.
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015). Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.
Lin, L.-J. (1992). Self-improving reactive agents based on reinforcement learning, planning and teaching. Machine learning, 8(3):293-321.
Ma, X., Xia, L., Zhou, Z., Yang, J., and Zhao, Q. (2020). Dsac: Distributional soft actor critic for risk-sensitive reinforcement learning. arXiv preprint arXiv:2004.14547.
Mai, V., Mani, K., and Paull, L. (2022). Sample efficient deep reinforcement learning via uncertainty estimation. arXiv preprint arXiv:2201.01666.
McFadden, D. et al. (1973). Conditional logit analysis of qualitative choice behavior. Institute of Urban and Regional Development, University of California.
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016). Asynchronous methods for deep reinforcement learning. In International conference on machine learning, pages 1928-1937. PMLR.