1. Amoui, M., Salehie, M., Mirarab, S., & Tahvildari, L. (2008, March). Adaptive action selection in autonomic software using reinforcement learning. In Fourth International Conference on Autonomic and Autonomous Systems (ICAS'08) (pp. 175-181). IEEE.
2. Arabnejad, H., Pahl, C., Jamshidi, P., & Estrada, G. (2017, May). A comparison of reinforcement learning techniques for fuzzy cloud auto-scaling. In 2017 17th IEEE/ACM international symposium on cluster, cloud and grid computing (CCGRID) (pp. 64-73). IEEE.
3. Aschoff, R., & Zisman, A. (2011). QoS-driven proactive adaptation of service composition. In Service-Oriented Computing: 9th International Conference, ICSOC 2011, Paphos, Cyprus, December 5-8, 2011 Proceedings 9 (pp. 421-435). Springer Berlin Heidelberg.
4. Barrett, E., Howley, E., & Duggan, J. (2013). Applying reinforcement learning towards automating resource allocation and application scalability in the cloud. Concurrency and computation: practice and experience, 25(12), 1656-1674.
5. Bu, X., Rao, J., & Xu, C. Z. (2012). Coordinated self-configuration of virtual machines and appliances using a model-free learning approach. IEEE transactions on parallel and distributed systems, 24(4), 681-690.
6. Caporuscio, M., D’Angelo, M., Grassi, V., & Mirandola, R. (2016). Reinforcement learning techniques for decentralized self-adaptive service assembly. In Service-Oriented and Cloud Computing: 5th IFIP WG 2.14 European Conference, ESOCC 2016, Vienna, Austria, September 5-7, 2016, Proceedings 5 (pp. 53-68). Springer International Publishing.
7. Chen, T., & Bahsoon, R. (2016). Self-adaptive and online qos modeling for cloud-based software services. IEEE Transactions on Software Engineering, 43(5), 453-475.
8. D'Ippolito, N., Braberman, V., Kramer, J., Magee, J., Sykes, D., & Uchitel, S. (2014, May). Hope for the best, prepare for the worst: multi-tier control for adaptive systems. In Proceedings of the 36th International Conference on Software Engineering (pp. 688-699).
9. Dulac-Arnold, G., Evans, R., van Hasselt, H., Sunehag, P., Lillicrap, T., Hunt, J., ... & Coppin, B. (2015). Deep reinforcement learning in large discrete action spaces. arXiv preprint arXiv:1512.07679.
10. Dutreilh, X., Kirgizov, S., Melekhova, O., Malenfant, J., Rivierre, N., & Truck, I. (2011, May). Using reinforcement learning for autonomic resource allocation in clouds: towards a fully automated workflow. In ICAS 2011, The Seventh International Conference on Autonomic and Autonomous Systems (pp. 67-74).
11. Filho, R. R., & Porter, B. (2017). Defining emergent software using continuous self-assembly, perception, and learning. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 12(3), 1-25.
12. Jamshidi, P., Cámara, J., Schmerl, B., Käestner, C., & Garlan, D. (2019, May). Machine learning meets quantitative planning: Enabling self-adaptation in autonomous robots. In 2019 IEEE/ACM 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) (pp. 39-50). IEEE.
13. Gheibi, O., Weyns, D., & Quin, F. (2021). Applying machine learning in self-adaptive systems: A systematic literature review. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 15(3), 1-37.
14. Kephart, J. O., & Chess, D. M. (2003). The vision of autonomic computing. Computer, 36(1), 41-50.
15. Klein, C., Maggio, M., Årzén, K. E., & Hernández-Rodriguez, F. (2014, May). Brownout: Building more robust cloud applications. In Proceedings of the 36th International Conference on Software Engineering (pp. 700-711).
16. De Lemos, R., Giese, H., Müller, H. A., Shaw, M., Andersson, J., Litoiu, M., ... & Wuttke, J. (2013). Software engineering for self-adaptive systems: A second research roadmap. In Software Engineering for Self-Adaptive Systems II: International Seminar, Dagstuhl Castle, Germany, October 24-29, 2010 Revised Selected and Invited Papers (pp. 1-32). Springer Berlin Heidelberg.
17. Lorido-Botran, T., Miguel-Alonso, J., & Lozano, J. A. (2014). A review of auto-scaling techniques for elastic applications in cloud environments. Journal of grid computing, 12, 559-592.
18. Mann, Z. Á. (2017). Resource optimization across the cloud stack. IEEE Transactions on Parallel and Distributed Systems, 29(1), 169-182.
19. Moustafa, A., & Zhang, M. (2014, June). Learning efficient compositions for QoS-aware service provisioning. In 2014 IEEE International Conference on Web Services (pp. 185-192). IEEE.
20. Nachum, O., Norouzi, M., Xu, K., & Schuurmans, D. (2017). Bridging the gap between value and policy based reinforcement learning. Advances in neural information processing systems, 30.
21. Ramirez, A. J., Jensen, A. C., & Cheng, B. H. (2012, June). A taxonomy of uncertainty for dynamically adaptive systems. In 2012 7th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) (pp. 99-108). IEEE.
22. Salehie, M., & Tahvildari, L. (2009). Self-adaptive software: Landscape and research challenges. ACM transactions on autonomous and adaptive systems (TAAS), 4(2), 1-42.
23. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
24. Silvander, J. (2019). Business process optimization with reinforcement learning. In Business Modeling and Software Design: 9th International Symposium, BMSD 2019, Lisbon, Portugal, July 1–3, 2019, Proceedings 9 (pp. 203-212). Springer International Publishing.
25. Sutton, R. S., McAllester, D., Singh, S., & Mansour, Y. (1999). Policy gradient methods for reinforcement learning with function approximation. Advances in neural information processing systems, 12.
26. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. MIT press.
27. Tesauro, G., Jong, N. K., Das, R., & Bennani, M. N. (2007). On the use of hybrid reinforcement learning for autonomic resource allocation. Cluster Computing, 10, 287-299.
28. Iglesia, D. G. D. L., & Weyns, D. (2015). MAPE-K formal templates to rigorously design behaviors for self-adaptive systems. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 10(3), 1-31.
