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Numerous solutions are proposed for the Traffic Signal Control (TSC) tasks aiming to provide efficient transportation and alleviate traffic congestion. Recently, promising results have been attained by Reinforcement Learning (RL) methods through trial and error in simulators, bringing confidence in solving cities' congestion problems. However, performance gaps still exist when simulator-trained policies are deployed to the real world. This issue is mainly introduced by the system dynamic difference between the training simulators and the real-world environments. In this work, we leverage the knowledge of Large Language Models (LLMs) to understand and profile the system dynamics by a prompt-based grounded action transformation to bridge the performance gap. Specifically, this paper exploits the pre-trained LLM's inference ability to understand how traffic dynamics change with weather conditions, traffic states, and road types. Being aware of the changes, the policies' action is taken and grounded based on realistic dynamics, thus helping the agent learn a more realistic policy. We conduct experiments on four different scenarios to show the effectiveness of the proposed PromptGAT's ability to mitigate the performance gap of reinforcement learning from simulation to reality (sim-to-real).
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This content will become publicly available on March 31, 2026
Constrained Reinforcement Learning for Fair and Environmentally Efficient Traffic Signal Controllers
Traffic signal controller (TSC) has a crucial role in managing traffic flow in urban areas. Recently, reinforcement learning (RL) models have received a great attention for TSC with promising results. However, these RL-TSC models still need to be improved for real-world deployment due to limited exploration of different performance metrics such as fair traffic scheduling or air quality impact. In this work, we introduce a constrained multi-objective RL model that minimizes multiple constrained objectives while achieving a higher expected reward. Furthermore, our proposed RL strategy integrates the peak and average constraint models to the RL problem formulation with maximum entropy off-policy models. We applied this strategy to a single TSC and a network of TSCs. As part of this constrained RL-TSC formulation, we discuss fairness and air quality parameters as constraints for the closed-loop control system optimization model at TSCs calledFAirLight. Our experimental analysis shows that the proposedFAirLightachieves a good traffic flow performance in terms of average waiting time while being fair and environmentally friendly. Our method outperforms the baseline models and allows a more comprehensive view of RL-TSC regarding its applicability to the real world.
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- Award ID(s):
- 1934568
- PAR ID:
- 10555548
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- ACM Journal on Autonomous Transportation Systems
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2833-0528
- Page Range / eLocation ID:
- 1 to 19
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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