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Łukasz Gorczyca

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2 papers

1 author row

AAMAS Conference 2026 Conference Paper

Autonomous Vehicles need Social Awareness to Find Optima in Multi-agent Reinforcement Learning Routing Games

Anastasia Psarou
Łukasz Gorczyca
Dominik Gawel
Rafal Kucharski

Previous work has shown that when multiple selfish Autonomous Vehicles(AVs)simultaneouslylearnoptimalroutingstrategiesusing Multi-Agent Reinforcement Learning (MARL), they may require a significant amount of time to converge to the optimal solution, equivalent to years of real-world commuting. We demonstrate that moving beyond the selfish component in the reward significantly relieves this issue. In particular, we introduce a reward signal based on the marginal cost matrix, which quantifies the impact of each individual action (route-choice) on the system (total travel time). This formulation reduces training time and improves convergence reliability. Experiments on both a toy network and the real-world Saint-Arnoult network show that the proposed reward improves individual and system travel times over the selfish reward baseline, and in thetoy network, enablesagents to reachthe optimal solution faster, indicating that incorporating social awareness (i. e. , including marginal costs in routing decisions) can enhance both system-wide and individual outcomes in future urban systems with AVs.

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NeurIPS Conference 2025 Conference Paper

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

Ahmet Onur Akman
Anastasia Psarou
Michał Hoffmann
Łukasz Gorczyca
Lukasz Kowalski
Paweł Gora
Grzegorz Jamróz
Rafal Kucharski

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 using 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 $\texttt{URB}$: Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles. $\texttt{URB}$ is a comprehensive benchmarking environment that unifies evaluation across 29 real-world traffic networks paired with realistic demand patterns. $\texttt{URB}$ comes with a catalog of predefined tasks, multi-agent RL (MARL) algorithm implementations, three baseline methods, domain-specific performance metrics, and a modular configuration scheme. Our results show that, despite the lengthy and costly training, state-of-the-art MARL algorithms rarely outperformed humans. The experimental results reported in this paper initiate the first leaderboard for MARL in large-scale urban routing optimization. They reveal that current approaches struggle to scale, emphasizing the urgent need for advancements in this domain.

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