Learning-based agent for Google Research Football

Overview

TiKick

License

1.Introduction

Learning-based agent for Google Research Football

Code accompanying the paper "TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations". [arxiv][videos]

2.Installation

pip install -r requirements.txt
pip install -e .

3.Evaluation with Trained Model

(a) First, you should download the trained model from Baidu Yun or Google Drive:

(b) Then, you should put the actor.pt under ./models/academy_3_vs_1_with_keeper/.

(c) Finally, you can go to the ./scripts/football folder and execute the evaluation script as below:

cd scripts/football
./evaluate.sh

Then the replay file will be saved into ./results/academy_3_vs_1_with_keeper/replay/.

  • Hyper-parameters in the evaluation script:
    • --replay_save_dir : the replay file will be saved in this directory
    • --model_dir : pre-trained model should be placed under this directory
    • --n_eval_rollout_threads : number of parallel envs for evaluating rollout
    • --eval_num : number of total evaluation times

4.Render with the Replay File

Once you obtain a replay file, you can convert it to a .avi file and watch the game. This can be easily done via:

cd scripts/football
python3 replay2video.py --replay_file ../../results/academy_3_vs_1_with_keeper/replay/your_path.dump

The video file will finally be saved to ./results/academy_3_vs_1_with_keeper/video/

5.Cite

Please cite our paper if you use our codes or our weights in your own work:

@misc{huang2021tikick,
    title={TiKick: Towards Playing Multi-agent Football Full Games from Single-agent Demonstrations},
    author={Shiyu Huang and Wenze Chen and Longfei Zhang and Ziyang Li and Fengming Zhu and Deheng Ye and Ting Chen and Jun Zhu},
    year={2021},
    eprint={2110.04507},
    archivePrefix={arXiv},
    primaryClass={cs.AI}
}
Owner
Tsinghua AI Research Team for Reinforcement Learning
Tsinghua AI Research Team for Reinforcement Learning (Creativity, Practicality and Optimist)
Tsinghua AI Research Team for Reinforcement Learning
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