mlsanityData to 14 Oct 2026

Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning

arXiv 2308.14284 ↗First posted 28 Aug 2023cs.AIcited by 36

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Abstract (arXiv)

Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and mitigate congestion waste. In recent, promising results have been attained by Reinforcement Learning (RL) methods through trial and error in simulators, bringing confidence in solving cities' congestion headaches. However, there still exist performance gaps when simulator-trained policies are deployed to the real world. This issue is mainly introduced by the system dynamic difference between the training simulator and the real-world environments. The Large Language Models (LLMs) are trained on mass knowledge and proved to be equipped with astonishing inference abilities. In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation. Accepting the cloze prompt template, and then filling in the answer based on accessible context, the pre-trained LLM's inference ability is exploited and applied to understand how weather conditions, traffic states, and road types influence traffic dynamics, being aware of this, the policies' action is taken and grounded based on realistic dynamics, thus help the agent learn a more realistic policy. We conduct experiments using DQN to show the effectiveness of the proposed PromptGAT's ability in mitigating the performance gap from simulation to reality (sim-to-real).

Key facts. Cited by 36 papers in the map (data through 14 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 28 Aug 2023.

Builds on

18 of 37 references.

References · 18

  1. 2019How Can We Know What Language Models Know?cited by 959
  2. 2018Learning Dexterous In-Hand Manipulationcited by 687
  3. 2014Deep Domain Confusion: Maximizing for Domain Invariancecited by 600
  4. 2020Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Surveycited by 413
  5. 2014Robots that can adapt like animalscited by 204
  6. 2018Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networkscited by 174
  7. 2017Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Graspingcited by 144
  8. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 138
  9. 2019CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenariocited by 115
  10. 2020Deep Reinforcement Learning for Intelligent Transportation Systems: A Surveycited by 98
  11. 2019Learning Phase Competition for Traffic Signal Controlcited by 87
  12. 2017Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulationcited by 36
6 more
  1. 2022Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learningcited by 28
  2. 2020Reinforced Grounded Action Transformation for Sim-to-Real Transfercited by 18
  3. 2020GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learningcited by 17
  4. 2020Stochastic Grounded Action Transformation for Robot Learning in Simulationcited by 16
  5. 2021Towards Real-World Deployment of Reinforcement Learning for Traffic Signal Controlcited by 6
  6. 2021Deep Reinforcement Q-Learning for Intelligent Traffic Signal Control with Partial Detectioncited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 10; 2025: 16; 2026: 8.

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  1. 2025Uncertainty Quantification and Confidence Calibration in Large Language Models: A Surveycited by 95
  2. 2023TrafficGPT: Viewing, Processing and Interacting with Traffic Foundation Modelscited by 45
  3. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 38
  4. 2024ChatSUMO: Large Language Model for Automating Traffic Scenario Generation in Simulation of Urban MObilitycited by 34
  5. 2025Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmapcited by 26
  6. 2024eTraM: Event-based Traffic Monitoring Datasetcited by 24
  7. 2024LLM Uncertainty Quantification through Directional Entailment Graph and Claim Level Response Augmentationcited by 23
  8. 2025Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directionscited by 23
  9. 2024CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Trafficcited by 22
  10. 2024Towards Explainable Traffic Flow Prediction with Large Language Modelscited by 20
  11. 2025Unraveling Spatio-Temporal Foundation Models via the Pipeline Lens: A Comprehensive Reviewcited by 10
  12. 2025Generative AI in Transportation Planning: A Surveycited by 8
24 more
  1. 2024CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Controlcited by 7
  2. 2024Re-boosting Self-Collaboration Parallel Prompt GAN for Unsupervised Image Restorationcited by 7
  3. 2024MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctionscited by 5
  4. 2025A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysiscited by 5
  5. 2025DeepShade: Enable Shade Simulation by Text-conditioned Image Generationcited by 4
  6. 2026Conformal Feedback Alignment: Quantifying Answer-Level Reliability for Robust LLM Alignmentcited by 4
  7. 2023Probabilistic Offline Policy Ranking with Approximate Bayesian Computationcited by 3
  8. 2024A Simple Framework Towards Vision-based Traffic Signal Control with Microscopic Simulationcited by 3
  9. 2025Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Controlcited by 2
  10. 2025Urban Computing in the Era of Large Language Modelscited by 2
  11. 2025cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blendingcited by 2
  12. 2025A Dual Large Language Models Architecture with Herald Guided Prompts for Parallel Fine Grained Traffic Signal Controlcited by 2
  13. 2026LLM-Guided Communication for Cooperative Multi-Agent Reinforcement Learningcited by 2
  14. 2025Agentic Episodic Controlcited by 1
  15. 2026Virtual Traffic Police: Large Language Model-Augmented Traffic Signal Control for Unforeseen Incidentscited by 1
  16. 2026LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Controlcited by 1
  17. 2026The Sim-to-Real Gap of Foundation Model Agents: A Unified MDP Perspectivecited by 1
  18. 2024Responsible AI in NLP: GUS-Net Span-Level Bias Detection Dataset and Benchmark for Generalizations, Unfairness, and Stereotypescited by 0
  19. 2025FedTDP: A Privacy-Preserving and Unified Framework for Trajectory Data Preparation via Federated Learningcited by 0
  20. 2025HALO: Hierarchical Reinforcement Learning for Large-Scale Adaptive Traffic Signal Controlcited by 0
  21. 2025Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Controlcited by 0
  22. 2026CuraLight: Debate-Guided Data Curation for LLM-Centered Traffic Signal Controlcited by 0
  23. 2026Large Language Models in Transportation Systems Management and Operations: From Text Reasoning to Multi-modal Decision Supportcited by 0
  24. 2026VLALight: Lightweight Vision-Language-Action Models for Emergency-Aware Traffic Signal Controlcited by 0