Prompt learning for Sim2Real under reinforcement learning

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We have created a docker image for your convenience

(Start sim-to-real for TSC by a single command)!

This docker code base contains three projects, first pull from docker hub:

docker pull danielda1/ugat:latest

docker run -it --name ugat_case danielda1/ugat:latest

For this repo's paper: AAAI24: Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning

cd /DaRL/PromptGAT

python sim2real.py

At the same time of using this Docker Image, you have the the readily prepared LibSignal

This is a multi-simulator-supported framework that provides easy-to-configure settings for sim-to-sim simulated sim-to-real training and testing. For details, please visit: https://darl-libsignal.github.io/

For LibSignal - Then go to the terminal:

cd /DaRL/LibSignal

python run.py

We have also included another sim-to-real for RL - TSC tasks:

CDC23: Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control (https://github.com/darl-libsignal/ugat)

Stay in the same docker environment, go to command line:

cd /DaRL/UGAT_Docker/

python sim2real.py

Description:

This repo is the code implementation of AAAI 2024 paper: Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
Or another version: LLM Powered Sim-to-real Transfer for Traffic Signal Control Before publish proceedings complete, You can find on an arXiv version here: https://arxiv.org/pdf/2308.14284.pdf

An illustration of our method (PromptGAT) compared to Vanilla GAT:

Detailed Structure of PromptGAT (for more introduction, please refer to our paper above):

This project is built on the code of the paper: "Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control," but the uncertainty module is annotated as not working.

The overall project is built based on LibSignal: https://darl-libsignal.github.io/

Instruction:

Please fill in your Open-AI key in the file sim2real_trainer.py to make sure the conversation with the language agent is successfully connected!

Please make sure to install the requirements.txt file before execution.

To execute the code, please execute the sim2real.py directly in the root folder.

Citation

If you find this work helpful, please cite us:

@inproceedings{da2024prompt,
  title={Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning},
  author={Da, Longchao and Gao, Minquan and Mei, Hao and Wei, Hua},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={38},
  number={1},
  pages={82--90},
  year={2024}
}