mlsanityData to 14 Oct 2026

Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method

arXiv 2204.12190 ↗First posted 26 Apr 2022cs.AIcited by 10

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Abstract (arXiv)

How to coordinate the communication among intersections effectively in real complex traffic scenarios with multi-intersection is challenging. Existing approaches only enable the communication in a heuristic manner without considering the content/importance of information to be shared. In this paper, we propose a universal communication form UniComm between intersections. UniComm embeds massive observations collected at one agent into crucial predictions of their impact on its neighbors, which improves the communication efficiency and is universal across existing methods. We also propose a concise network UniLight to make full use of communications enabled by UniComm. Experimental results on real datasets demonstrate that UniComm universally improves the performance of existing state-of-the-art methods, and UniLight significantly outperforms existing methods on a wide range of traffic situations.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 11 of its 37 references that are in the map. First posted on arXiv 26 Apr 2022.

Builds on

11 of 37 references.

References · 11

  1. 2017Attention Is All You Needcited by 60,361
  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,258
  5. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  6. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 138
  7. 2019CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenariocited by 115
  8. 2019Learning Phase Competition for Traffic Signal Controlcited by 87
  9. 2020AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Controlcited by 62
  10. 2019Diagnosing Reinforcement Learning for Traffic Signal Controlcited by 33
  11. 2019Learning an Interpretable Traffic Signal Control Policycited by 20

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 0; 2025: 5; 2026: 1.

10 papers.

  1. 2022The Real Deal: A Review of Challenges and Opportunities in Moving Reinforcement Learning-Based Traffic Signal Control Systems Towards Realitycited by 9
  2. 2023Towards Computationally Efficient Responsibility Attribution in Decentralized Partially Observable MDPscited by 3
  3. 2023Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approachcited by 3
  4. 2025A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Controlcited by 3
  5. 2025cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blendingcited by 2
  6. 2023Real-Time Network-Level Traffic Signal Control: An Explicit Multiagent Coordination Methodcited by 1
  7. 2025Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Modelscited by 0
  8. 2025Learning Multi-Robot Coordination through Locality-Based Factorized Multi-Agent Actor-Critic Algorithmcited by 0
  9. 2025Smart Traffic Signals: Comparing MARL and Fixed-Time Strategiescited by 0
  10. 2026CuraLight: Debate-Guided Data Curation for LLM-Centered Traffic Signal Controlcited by 0