ARCHIVES

1. Li, Y. (2018). Deep reinforcement learning: An overview. arXiv preprint arXiv:1701.07274. https://arxiv.org/abs/1701.07274
2. Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., & Koltun, V. (2017). CARLA: An open urban driving simulator. Proceedings of the 1st Annual Conference on Robot Learning, 78, 1–16. https://arxiv.org/abs/1711.03938
3. Kiran, B. R., Sobh, I., Talpaert, V., Mannion, P., Al Sallab, A. A., Yogamani, S., & Pérez, P. (2021). Deep reinforcement learning for autonomous driving: A survey. IEEE Transactions on Intelligent Transportation Systems, 23(6), 4909–4926. https://doi.org/10.1109/TITS.2021.3054625
4. Liu, Y., Xu, D., Liu, X., & Wang, J. (2022). Sim-to-real transfer challenges in deep reinforcement learning for robotics. Journal of Intelligent & Robotic Systems.
5. Zhao, W., Peña Queralta, J., & Westerlund, T. (2020). Sim to Real Transfer in Deep Reinforcement Learning for Robotics: A Survey. arXiv. https://arxiv.org/abs/2009.13303
6. Sharma, A., Gomez, F., & Muvva, V. (2023). Facilitating sim to real transfer and deployment in mobile robot navigation via high fidelity simulation and domain adaptation. Proceedings in Robotics and Autonomous Systems.
7. [Muratore, F., Ramos, F., Turk, G., Yu, W., Gienger, M., & Peters, J. (2022). Robot learning from randomized simulations: A review. Frontiers in Robotics and AI, 9, Article 799893. https://doi.org/10.3389/frobt.2022.799893
8. Peng, X. B., Andrychowicz, M., Zaremba, W., & Abbeel, P. (2017). Sim to real transfer of robotic control with dynamics randomization. IEEE International Conference on Robotics and Automation (ICRA), 23–30.
9. Osiński, B., Jakubowski, A., Miłoś, P., Zięcina, P., Galias, C., Homoceanu, S., & Michalewski, H. (2019). Simulation based reinforcement learning for real world autonomous driving. arXiv preprint arXiv:1911.12905.
10. Tai, L., Paolo, G., & Liu, M. (2017). Virtual to real deep reinforcement learning: Continuous control of mobile robots for mapless navigation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
11. Eßer, J. (2023). Robotic Challenges – The Road Ahead. Lamarr Institute Blog. Retrieved from lamarr institute.org
12. Ilahi, I., Usama, M., Qadir, J., Janjua, M. U., Al Fuqaha, A., Hoang, D. T., & Niyato, D. (2020). Challenges and countermeasures for adversarial attacks on deep reinforcement learning. arXiv preprint.
13. Lei, Y., Ye, D., Shen, S., Sui, Y., Zhu, T., & Zhou, W. (2022). New challenges in reinforcement learning: A survey of security and privacy. arXiv preprint.
14. Wikipedia contributors. (2025, July). Deep reinforcement learning. In Wikipedia. https://en.wikipedia.org/wiki/Deep_reinforcement_learning
15. Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
16. Puterman, M. L. (1994). Markov decision processes: Discrete stochastic dynamic programming. Wiley-Interscience.
17. Kaelbling, L. P., Littman, M. L., & Moore, A. W. (1996). Reinforcement learning: A survey. Journal of Artificial Intelligence Research, 4, 237–285.
18. Mnih, V., Kavukcuoglu, K., Silver, D., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518(7540), 529–533. https://doi.org/10.1038/nature14236
19. Schulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347.
20. Lillicrap, T. P., Hunt, J. J., Pritzel, A., et al. (2015). Continuous control with deep reinforcement learning. arXiv preprint arXiv:1509.02971.
21. Hossain, J. (2023). Autonomous driving with deep reinforcement learning in CARLA simulation. arXiv preprint.
22. (Springer study) Anonymous Authors. (2021). Deep reinforcement learning based control for autonomous vehicles in CARLA. Multimedia Tools and Applications, 80, Article 11437.
23. Bergasa, C. G.-H., Gutiérrez, R., & Díaz Díaz, A. (2021). Deep reinforcement learning based control for autonomous vehicles in CARLA. Multimedia Tools and Applications, 80, Article 11437.
24. Jaeger, B., Dauner, D., Beißwenger, J., et al. (2025). CaRL: Learning scalable planning policies with simple rewards. arXiv preprint.
25. Abouelazm, A., Michel, J., & Zoellner, J. M. (2024). A review of reward functions for reinforcement learning in the context of autonomous driving. arXiv preprint.
26. Mostafa, R. R., Sakr, R. H., & Rashad, M. Z. (2021). Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm. PLOS ONE, 16(6), e0252754.
27. Nehme, G., & Deo, T. Y. (2023). Safe navigation: Training autonomous vehicles using deep reinforcement learning in CARLA. arXiv preprint.
28. Li, Q., Jia, X., Wang, S., & Yan, J. (2024). Think2Drive: Efficient reinforcement learning by thinking in latent world model for quasi-realistic autonomous driving (in CARLA v2). arXiv preprint.
29. MDPI Authors. (2025). A comparative study of deep reinforcement learning algorithms for driving performance. Applied Sciences, 15(12), Article 6838.