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Author: backpropper

README.md

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+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](http://www.google.com "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
+------------------
+```bash
+$ 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 and Cho, Kyunghyun},
+    title = {Capacity, Bandwidth, and Compositionality in Emergent Language Learning},
+    year = {2020},
+    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},
+    numpages = {9},
+    keywords = {Multi-agent communication, Compositionality, Emergent languages},
+    location = {Auckland, New Zealand},
+    series = {AAMAS ’20},
+    url = {https://arxiv.org/abs/1910.11424},
+}
+```