mlsanityData to 14 Oct 2026

GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents

arXiv 2407.10811 ↗First posted 15 Jul 2024cs.MA cs.AI cs.LGcited by 1

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Currently, traffic signal control (TSC) methods based on reinforcement learning (RL) have proven superior to traditional methods. However, most RL methods face difficulties when applied in the real world due to three factors: input, output, and the cycle-flow relation. The industry's observable input is much more limited than simulation-based RL methods. For real-world solutions, only flow can be reliably collected, whereas common RL methods need more. For the output action, most RL methods focus on acyclic control, which real-world signal controllers do not support. Most importantly, industry standards require a consistent cycle-flow relationship: non-decreasing and different response strategies for low, medium, and high-level flows, which is ignored by the RL methods. To narrow the gap between RL methods and industry standards, we innovatively propose to use industry solutions to guide the RL agent. Specifically, we design behavior cloning and curriculum learning to guide the agent to mimic and meet industry requirements and, at the same time, leverage the power of exploration and exploitation in RL for better performance. We theoretically prove that such guidance can largely decrease the sample complexity to polynomials in the horizon when searching for an optimal policy. Our rigid experiments show that our method has good cycle-flow relation and superior performance.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 18 of its 45 references that are in the map. First posted on arXiv 15 Jul 2024.

Builds on

18 of 45 references.

References · 18

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2020Curriculum Learning for Reinforcement Learning Domains: A Framework and Surveycited by 373
  4. 2020Bypassing the Monster: A Faster and Simpler Optimal Algorithm for Contextual Bandits under Realizabilitycited by 115
  5. 2022Jump-Start Reinforcement Learningcited by 112
  6. 2019Learning Phase Competition for Traffic Signal Controlcited by 87
  7. 2019A Survey on Traffic Signal Control Methodscited by 79
  8. 2018Deep Reinforcement Learning for Traffic Light Control in Vehicular Networkscited by 52
  9. 2023Learning to Generate Better Than Your LLMcited by 49
  10. 2019Contextual Bandits with Continuous Actions: Smoothing, Zooming, and Adaptingcited by 42
  11. 2021Deep Multiagent Reinforcement Learning: Challenges and Directionscited by 42
  12. 2017Traffic Light Control Using Deep Policy-Gradient and Value-Function Based Reinforcement Learningcited by 33
6 more
  1. 2017Deep Deterministic Policy Gradient for Urban Traffic Light Controlcited by 30
  2. 2017Adaptive Traffic Signal Control: Deep Reinforcement Learning Algorithm with Experience Replay and Target Networkcited by 16
  3. 2024X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learnercited by 10
  4. 2023DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledgecited by 9
  5. 2024CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Controlcited by 7
  6. 2013Distributed Online Learning via Cooperative Contextual Banditscited by 5

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctionscited by 5

Similar, not cited

Close in wording, no citation either way

  1. 2025Single-agent Reinforcement Learning Model for Regional Adaptive Traffic Signal Control
  2. 2023A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
  3. 2023Traffic Signal Control Using Lightweight Transformers: An Offline-to-Online RL Approach
  4. 2026Evaluating the Robustness of Reinforcement Learning based Adaptive Traffic Signal Control
  5. 2025Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse
  6. 2026A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control
  7. 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
  8. 2024iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement