I am a last year student of MSc in Machine Learning and Cybersecurity for Internet-Connected Systems at UPC.
My research is dedicated to creating intelligent and autonomous systems by integrating principles from mathematical programming, game theory, and reinforcement learning. I develop algorithms designed to solve complex decision-making problems, with a particular focus on scenarios governed by ranked metrics. My research is grounded in autonomous driving applications. I currently work on these things with Georgios Bakirtzis.
Autonomous vehicles must balance ranked objectives, such as minimizing travel time, ensuring safety, and coordinating with traffic. Games of ordered preference effectively model these interactions but become computationally intractable as the time horizon, number of players, or number of preference levels increase. While receding horizon frameworks mitigate long-horizon intractability by solving sequential shorter games, often warm-started, they do not resolve the complexity growth inherent in existing methods for solving games of ordered preference. This paper introduces a solution strategy that avoids excessive complexity growth by approximating solutions using lexicographic iterated best response (IBR) in receding horizon, termed "lexicographic IBR over time." Lexicographic IBR over time uses past information to accelerate convergence. We demonstrate through simulated traffic scenarios that lexicographic IBR over time efficiently computes approximate-optimal solutions for receding horizon games of ordered preference, converging towards generalized Nash equilibria.
@misc{molins2025approximatesolutionsgamesordered,title={Approximate solutions to games of ordered preference},author={{de las Heras Molins}, P. and Roy-Almonacid, E. and Lee, D. H. and Peters, L. and Fridovich-Keil, D. and Bakirtzis, G.},year={2025},eprint={2507.11021},archiveprefix={arXiv},primaryclass={eess.SY},url={https://arxiv.org/abs/2507.11021},}
