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This is the code reference for the main process of GAVE accepted by SIGIR'25.
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The code provided is for reference only.
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Because the dataset is too large to upload, you need to add the dataset before it can run
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GAVE sturcture: code/bidding_train_env/baseline/dt/dt.py -> class GAVE
Implementation Details:
- Codebase: Lightweight reference implementation using a variant of the AuctionNet benchmark, i.e., AIGB
- Data: A sample dataset for training is available at
Data/trajectory/trajectory_data.csv. Test data was omitted due to size constraints.
Execution:
- Add full datasets with structure:
GAVE folder/ --data/ ----traffic/ ------period-x.csv # Test data ----trajectory/ ------trajectory_data.csv # Train data
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Run:
python code/main/main_train_test.py
If you feel our work is insightful and want to use the code or cite our paper, please add the following citation to your paper references.
@article{gao2025generative,
title={Generative Auto-Bidding with Value-Guided Explorations},
author={Gao, Jingtong and Li, Yewen and Mao, Shuai and Jiang, Peng and Jiang, Nan and Wang, Yejing and Cai, Qingpeng and Pan, Fei and Gai, Kun and An, Bo and others},
journal={arXiv preprint arXiv:2504.14587},
year={2025}
}