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Found 7 papers, 4 papers with code
Privacy-preserving Fine-tuning of Large Language Models through Flatness
The privacy concerns associated with the use of Large Language Models (LLMs) have grown recently with the development of LLMs such as ChatGPT.
CityFlowER: An Efficient and Realistic Traffic Simulator with Embedded Machine Learning Models
Addressing these limitations, we introduce CityFlowER, an advancement over the existing CityFlow simulator, designed for efficient and realistic city-wide traffic simulation.
Open-TI: Open Traffic Intelligence with Augmented Language Model
Transportation has greatly benefited the cities' development in the modern civilization process.
Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
In this paper, we present Probabilistic Offline Policy Ranking (POPR), a framework to address OPR problems by leveraging expert data to characterize the probability of a candidate policy behaving like experts, and approximating its entire performance posterior distribution to help with ranking.
Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
In this work, we leverage LLMs to understand and profile the system dynamics by a prompt-based grounded action transformation.
Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control
Traffic signal control (TSC) is a complex and important task that affects the daily lives of millions of people.
LibSignal: An Open Library for Traffic Signal Control
This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks.
