1. Alves, J.C., Mateus, G.R.: Multi-echelon supply chains with uncertain seasonal demands and lead times using deep reinforcement learning. Submitted (2021)
2. Bergstra, J., Bardenet, R., Bengio, Y., Kégl, B.: Algorithms for hyper-parameter optimization. In: Proceedings of the 24th International Conference on Neural Information Processing Systems. NIPS 2011, Red Hook, NY, USA, pp. 2546–2554. Curran Associates Inc. (2011)
3. Colas, C., Sigaud, O., Oudeyer, P.Y.: A Hitchhiker’s guide to statistical comparisons of reinforcement learning algorithms. In: ICLR Worskhop on Reproducibility, Nouvelle-Orléans, United States, May 2019. https://hal.archives-ouvertes.fr/hal-02369859
4. Fujimoto, S., van Hoof, H., Meger, D.: Addressing function approximation error in actor-critic methods. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, 10–15 Jul 2018, vol. 80, pp. 1587–1596. PMLR (2018). http://proceedings.mlr.press/v80/fujimoto18a.html
5. Geevers, K.: Deep reinforcement learning in inventory management. Master’s thesis, University of Twente, December 2020. http://essay.utwente.nl/85432/