Paweł Gora

List of main publications and preprints

1. arXiv

   URB - Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles

   Akman, Ahmet Onur, Psarou, Anastasia, Hoffmann, Michał, Gorczyca, Łukasz, Kowalski, Łukasz, Gora, Paweł, Jamróz, Grzegorz, and Kucharski, Rafał

   arXiv preprint arXiv:2505.17734 2025

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   Connected Autonomous Vehicles (CAVs) promise to reduce congestion in future urban networks, potentially by optimizing their routing decisions. Unlike for human drivers, these decisions can be made with collective, data-driven policies, developed by machine learning algorithms. Reinforcement learning (RL) can facilitate the development of such collective routing strategies, yet standardized and realistic benchmarks are missing. To that end, we present \our: Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles. \our is a comprehensive benchmarking environment that unifies evaluation across 29 real-world traffic networks paired with realistic demand patterns. \our comes with a catalog of predefined tasks, four state-of-the-art multi-agent RL (MARL) algorithm implementations, three baseline methods, domain-specific performance metrics, and a modular configuration scheme. Our results suggest that, despite the lengthy and costly training, state-of-the-art MARL algorithms rarely outperformed humans. Experimental results reported in this paper initiate the first leaderboard for MARL in large-scale urban routing optimization and reveal that current approaches struggle to scale, emphasizing the urgent need for advancements in this domain.

2. Explainability of surrogate models for traffic signal control

   Gora, Pawel, Bogucki, Dominik, and Bolum, M Latif

   2023

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   Traffic signal control is one of the most important means of managing and optimizing road transport in cities. Developing a new generation of efficient traffic signal control algorithms is challenging and it is expected that methods based on AI may bring advantages. However, explainability and interpretability of these techniques are often limited and sometimes it might be difficult even for traffic engineers to understand the decisions taken by such systems. In this chapter we focus on explainability of one of the AI-based techniques used for traffic signal-control which employs evolutionary algorithms to find heuristically optimal signal settings for given traffic conditions, and surrogate models based on graph neural networks to evaluate the quality of signal settings much faster than by traffic simulations. The considered surrogate model is a graph neural network in which the topology of connections between neurons is built based on the topology of the road network graph (neurons correspond to intersections with traffic signals, or road segments between the intersections). Therefore, it can be suspected that analyzing behaviour of these neural networks may also bring better understanding of the urban road traffic and the spatio-temporal impact of traffic conditions on various intersections. In order to investigate the effects of input features on the output of the graph neural networks, we calculated the Shapley values and applied the Zorro method. Thanks to applying these techniques, it was possible to assess importance of intersections and their impact on the times of waiting on red signals in the considered areas on points from randomly generated datasets and on datasets with 500settings found using genetic algorithm (considered as close to local optima). It turned out that both methods produce quite consistent results. Thanks to them, it was possible to identify the most critical intersections in the road network topology which might be important from the traffic engineering perspective in tasks related to traffic signal control.

3. OE

   Quantum games and interactive tools for quantum technologies outreach and education

   Seskir, Zeki C, Migdał, Piotr, Weidner, Carrie, Anupam, Aditya, Case, Nicky, Davis, Noah, Decaroli, Chiara, Ercan, İlke, Foti, Caterina, Gora, Paweł, and others,

   Optical Engineering 2022

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   We provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the quantum community in recent years. We present selected tools as described by their developers, including “Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, ScienceAtHome, and the Virtual Quantum Optics Laboratory.” In addition, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population.