Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912780188123136 |
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| author | Pei, Shuwei Borger, Joran Kosay, Arda Sayin, Muhammed O. Ahmed, Saeed |
| author_facet | Pei, Shuwei Borger, Joran Kosay, Arda Sayin, Muhammed O. Ahmed, Saeed |
| contents | Learning-based traffic signal control is typically optimized for average performance under a few nominal demand patterns, which can result in poor behavior under atypical traffic conditions. To address this, we develop a distributionally robust multi-agent reinforcement learning framework for signal control on a 3x3 urban grid calibrated from a contiguous 3x3 subarea of central Athens covered by the pNEUMA trajectory dataset (Barmpounakis and Geroliminis, 2020). Our approach proceeds in three stages. First, we train a baseline multi-agent RL controller in which each intersection is governed by a proximal policy optimization agent with discrete signal phases, using a centralized training, decentralized execution paradigm. Second, to capture demand uncertainty, we construct eight heterogeneous origin-destination-based traffic scenarios-one directly derived from pNEUMA and seven synthetically generated-to span a wide range of spatial and temporal demand patterns. Over this scenario set, we train a contextual-bandit worst-case estimator that assigns mixture weights to estimate adversarial demand distributions conditioned on context. Finally, without modifying the controller architecture, we fine-tune the baseline multi-agent reinforcement learning agents under these estimated worst-case mixtures to obtain a distributionally robust multi-agent reinforcement learning controller. Across all eight scenarios, as well as on an unseen validation network based on the Sioux Falls configuration, the distributionally robust multi-agent reinforcement learning controller consistently reduces horizon-averaged queues and increases average speeds relative to the baseline, achieving up to 51% shorter queues and 38% higher speeds on the worst-performing scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_18558 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control Pei, Shuwei Borger, Joran Kosay, Arda Sayin, Muhammed O. Ahmed, Saeed Systems and Control Learning-based traffic signal control is typically optimized for average performance under a few nominal demand patterns, which can result in poor behavior under atypical traffic conditions. To address this, we develop a distributionally robust multi-agent reinforcement learning framework for signal control on a 3x3 urban grid calibrated from a contiguous 3x3 subarea of central Athens covered by the pNEUMA trajectory dataset (Barmpounakis and Geroliminis, 2020). Our approach proceeds in three stages. First, we train a baseline multi-agent RL controller in which each intersection is governed by a proximal policy optimization agent with discrete signal phases, using a centralized training, decentralized execution paradigm. Second, to capture demand uncertainty, we construct eight heterogeneous origin-destination-based traffic scenarios-one directly derived from pNEUMA and seven synthetically generated-to span a wide range of spatial and temporal demand patterns. Over this scenario set, we train a contextual-bandit worst-case estimator that assigns mixture weights to estimate adversarial demand distributions conditioned on context. Finally, without modifying the controller architecture, we fine-tune the baseline multi-agent reinforcement learning agents under these estimated worst-case mixtures to obtain a distributionally robust multi-agent reinforcement learning controller. Across all eight scenarios, as well as on an unseen validation network based on the Sioux Falls configuration, the distributionally robust multi-agent reinforcement learning controller consistently reduces horizon-averaged queues and increases average speeds relative to the baseline, achieving up to 51% shorter queues and 38% higher speeds on the worst-performing scenarios. |
| title | Distributionally Robust Multi-Agent Reinforcement Learning for Intelligent Traffic Control |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2512.18558 |