backpropper / cbc-emecom

Code repository for Capacity, Bandwidth, and Compositionality in Emergent Language Learning (AAMAS 2020)

Capacity, Bandwidth, and Compositionality in Emergent Language Learning

Code repository of the models described in the paper accepted at AAMAS 2020 Capacity, Bandwidth, and Compositionality in Emergent Language Learning.

Dependencies

Python

• Python>=3.6
• PyTorch>=1.2

GPU

• CUDA>=10.1
• cuDNN>=7.6

Running code

$ python main.py --num-binary-messages 24 --num-digits 6 --embedding-size-sender 40 --project-size-sender 60 --num-lstm-sender 300 --num-lstm-receiver 325 --embedding-size-receiver 125 --save-str <SAVE_STR>

where num-binary-messages is the bandwidth, num-digits is the number of concepts, and <SAVE_STR> is the filename.

License

This project is licensed under the terms of the MIT license.

Citation

If you find the resources in this repository useful, please consider citing:

@inproceedings{resnick*2020cap,
    author = {Resnick*, Cinjon and Gupta*, Abhinav and Foerster, Jakob and Dai, Andrew M. and Cho, Kyunghyun},
    title = {Capacity, Bandwidth, and Compositionality in Emergent Language Learning},
    year = {2020},
    isbn = {9781450375184},
    publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
    address = {Richland, SC},
    booktitle = {Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems},
    pages = {1125–1133},
    numpages = {9},
    keywords = {emergent languages, compositionality, multi-agent communication},
    location = {Auckland, New Zealand},
    series = {AAMAS ’20},
    url = {http://www.ifaamas.org/Proceedings/aamas2020/pdfs/p1125.pdf}
}