Title: Role-aware Multi-agent Reinforcement Learning for Coordinated Emergency Traffic Control
Venue: NeurIPS 2025
Accepted: yes
Keywords: Reinforcement Learning
Abstract: Emergency traffic control presents an increasingly critical challenge, requiring seamless coordination among emergency vehicles, regular vehicles, and traffic lights to ensure efficient passage for all vehicles. Existing models primarily only focus on traffic light control, leaving emergency and regular vehicles prone to delay due to the lack of navigation strategies. To address this issue, we propose the ***R*ole-aware *M*ulti-agent *T*raffic *C*ontrol (RMTC)** framework, which dynamically assigns appropriate roles to traffic components for better cooperation by considering their relations with emergency vehicles and adaptively adjusting their policies. Specifically, RMTC introduces a *Heterogeneous Temporal Traffic Graph (HTTG)* to model the spatial and temporal relationships among all traffic components (traffic lights, regular and emergency vehicles) at each time step. Furthermore, we develop a *Dynamic Role Learning* model to infer the evolving roles of traffic lights and regular vehicles based on HTTG. Finally, we present a *Role-aware Multi-agent Reinforcement Learning* approach that learns traffic policies conditioned on the dynamically roles. Extensive experiments across four public traffic scenarios show that RMTC outperforms existing traffic light control methods by significantly reducing emergency vehicle travel time, while effectively preserving traffic efficiency for regular vehicles. The code is released at [https://github.com/mingchenghexi/RMTC](https://github.com/mingchenghexi/RMTC).


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Role-aware Multi-agent Traffic Control (RMTC), a novel framework designed to enhance traffic coordination in scenarios involving emergency vehicles. RMTC introduces a Heterogeneous Temporal Traffic Graph (HTTG) to comprehensively capture the spatial-temporal interactions among traffic lights, regular vehicles, and emergency vehicles. Building on this representation, the framework incorporates a Dynamic Role Learning module that adaptively infers the roles of different traffic agents based on evolving traffic conditions. These roles are then leveraged in a Role-aware Multi-agent Reinforcement Learning algorithm to guide decision-making and coordination. Through extensive evaluations on four benchmark scenarios, RMTC demonstrates superior performance in reducing emergency vehicle delays while maintaining overall traffic efficiency, highlighting the benefits of incorporating dynamic roles into multi-agent traffic control.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \nS1. The paper is generally well-organized, with a logical flow of methodology. The equations and definitions are formal and clear.\n\nS2. The authors explicitly formulate the coordination of emergency vehicles, regular vehicles, and traffic lights as a joint multi-agent decision-making problem, which is a relatively new perspective in traffic control and provides new insights for related research.\n\nS3. The proposed integration of a heterogeneous temporal traffic graph and role-aware reinforcement learning is reasonable and effective. The experiments are sufficient, the settings and data preparation are well-illustrated, and the results are well-discussed.\n\nWeaknesses: \nW1. The paper introduces role-aware reinforcement learning, but it is unclear how different roles actually lead to different behavior patterns in agents.\n\nW2. The multi-agent modeling does not provide enough details, especially about how the Dynamic Role Learning module is trained, making it hard to fully understand or reproduce.\n\nW3. Some formulas in the manuscript are incomplete or simplified. For example, Eq.11 assumes only one EMV in the scenario, but the paper does not clarify how the intrinsic reward would be computed when multiple EMVs are present.\n\nQUESTIONS:\nQ1. Could the authors explain how role features are used to produce distinct behaviors across roles, and how this is reflected in the policy learning process?\n\nQ2. Please provide more detailed descriptions for the training of the Dynamic Role Learning module. This would help readers better understand and reproduce the approach.\n\nQ3. How does the framework handle scenarios with multiple emergency vehicles? Specifically, how is the intrinsic reward in Eq.11 extended or adapted to such cases? Clarification on this point would strengthen the technical rigor of the paper.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-25"}

comments: SUMMARY:
The paper introduces RMTC, a role-aware multi-agent reinforcement learning framework for emergency traffic control. It dynamically assigns roles to traffic lights and vehicles using a Temporal Traffic Graph, enabling coordinated behavior based on EMV position and trajectory. The method is tested against several benchmarks demonstrating an improvement in the EMV travel time without hurting the overall traffic efficiency.

STRENGTHS AND WEAKNESSES:
Strengths:
1. Interesting framework, unifying both signal control and navigation.
2. Results support the claim that this approach can benefit EMVs and should be explored further.

Weaknesses:
1. Limited modeling as EMVs are part of the system and treated as such, instead of allowing for individual decisions.
2. Scalability of the method is unclear. two aspects are relevant, how the method scales with the number of intersections (that seems to scale at least to small-medium networks), and how it scales with growing number of EMVs.

QUESTIONS:
1. Mainly about the sensitivity and scalability of the method, I am not sure how well it would scale to a real-life problem and if not what are the limitations.

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-30

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel framework called RMTC (Role-aware Multi-agent Traffic Control) that coordinates emergency vehicles (EMVs), regular vehicles (REVs), and traffic lights for efficient traffic control during emergencies. It introduces a Heterogeneous Temporal Traffic Graph (HTTG) to represent spatial-temporal relations among traffic agents and employs a dynamic role-learning mechanism to adaptively assign behaviors. A role-aware reinforcement learning model is trained using both extrinsic and intrinsic rewards derived from EMV influence. Experiments on four datasets (synthetic and real-world) show improved EMV travel time and overall traffic efficiency compared to baselines.\n\nSTRENGTHS AND WEAKNESSES:\nThe studied problem is interesting and important. The strengths: 1) Problem Setup: Formulates a novel multi-agent emergency traffic control problem that jointly coordinates EMVs, REVs, and traffic lights, beyond conventional traffic light-only control; 2) HTTG + Role Learning: Proposes the Heterogeneous Temporal Traffic Graph to model time-varying interactions and designs a dynamic role learning module based on mutual information maximization and role consistency; 3) Role-aware Reinforcement Learning: Combines role-conditioned observations with intrinsic and extrinsic rewards to guide traffic control policies: 4) Empirical Validation: Demonstrates clear improvements on EMV and REV travel time across multiple datasets, including ablation and stress testing under traffic load variations. Weaknesses: 1) The related work section is not comprehensive and overlooks many recent studies. 2) It’s not clear either in Figure 2 or the main content how the dynamic role influences the reinforcement learning process; 3) There is no latest traffic signal control baseline methods or methods specifically designed for traffic signal control with emergency vehicles, such as EMVLight or RECAL; 4) The paper does not report variance or error bars across different random seeds, limiting confidence in the reliability of performance gains; 5) The number of agents used in each task is not specified. If each vehicle requires an independent policy, the computational burden could be substantial. This important detail should be clearly addressed in the paper.\n\nQUESTIONS:\n1) While the proposed role-learning loss appears intuitively plausible, it remains largely heuristic and lacks theoretical justification. In particular, the paper does not explain why maximizing mutual information between role embeddings and EMV state features would necessarily lead to better coordination or policy learning.  Can you showcase how the dynamic role influences the reinforcement learning process? 2) The paper introduces a role embedding mechanism, but lacks evidence that these embeddings are meaningful or useful. Could you please elaborate further on this point? 3)  The interaction and potential conflicts between the different loss components (e.g., role consistency vs. responsiveness to EMVs) are not analyzed or theoretically grounded. 4) While the proposed framework involves reinforcement learning as a core optimization engine, the paper lacks critical evidence of actual policy learning. There are no training curves, no sample efficiency comparisons, and no analyses of training stability, convergence, or the effects of reward shaping.\n\nLIMITATIONS:\nYes, the authors discussed limitations in the appendix. However, it would be better to discuss some limitations in the main body.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes RMTC, a role-aware multi-agent RL framework that jointly controls traffic-lights, regular vehicles, and emergency vehicles (EMVs). It builds a heterogeneous temporal traffic graph, learns dynamic role embeddings driven by EMV position/trajectory, and adds a role-aware intrinsic reward. Experiments on two synthetic and two real SUMO scenarios show 4 – 26 % EMV travel-time reduction while keeping regular traffic efficient.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- Addresses a practically important but under-studied three-party coordination problem.\n- Solid technical integration: graph message passing + mutual-information role learning + intrinsic reward.\n- Thorough and towards real-world experiments setups: four benchmarks, ablations, congestion, and multi-EMV stress tests, code released.\n- Empirical gains over strong RL baselines (rMAPPO, X-Light) on both EMV and regular travel time.\n\n**Weaknesses**\n\n- Novelty incremental: many elements (GNN, MI role learning, PPO) exist in prior MARL/traffic-RL work like [1] and its prior works; contribution is mainly a combination.\n- Unclear evaluation: \n   - It is unclear how the dataset is split into train & test to report the evaluation metrics.\n   - It is unclear how the method performs under normal scenarios\n- Scalability unclear beyond ~16 intersections; compute overhead of graph & MI terms not reported.\n- Assumes perfect sensing, V2X comms, and strict driver compliance, which may limit its real-world application.\n- Related Work:\n  - Related work lacks recent attention-based or graph-meta RL traffic control methods and possibly role-based MARL\n- Poor writing quality: \n  - several typos (“Tough”, “implementtation”)\n  - Equation (14), it is unclear whether V is value function of extrinsic reward only or extr+intr\n  - missing reference to PPO by shulman et al.,\n  - A.2 what is Rl-base → RL-based?\n\n[1] Goel, H., Omama, M., Chalaki, B., Tadiparthi, V., Pari, E. M., & Chinchali, S. (2025). R3DM: Enabling Role Discovery and Diversity Through Dynamics Models in Multi-agent Reinforcement Learning. arXiv preprint arXiv:2505.24265.\n\nQUESTIONS:\n- Shouldn’t there be a rule-based baseline that prioritizes EMV completely to get an idea about the shortest time for EMV?\n- How does RMTC scale up to a large-city map? How to apply this method to real-world scenarios?\n- How are the baselines compared? What factors are controlled, and how were hyperparameters tuned separately for each baseline? Please clarify your controlled experiments.\n- How to handle contentions among many simultaneous emergencies?\n\nLIMITATIONS:\n- Framework only activates when EMV is detected; benefits vanish in normal conditions.\n- Relies on idealized communication, accurate, low-latency global state; noisy detection or non-cooperative drivers may break assumptions.\n- It is hard to gauge its contribution to the RL or role-based MARL without experiments in other domains, given that this paper is evaluated in the RL track.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-03"}