Juiced and Ready to Predict Private Information in Deep Cooperative Reinforcement Learning, Eugene Lim★, Bing Cai Kok★, Songli Wang★, Joshua Lee★, Harold Soh★, ACM/IEEE International Conference on Human-Robot Interaction (HRI)

In human-robot collaboration settings, each agent often has access to private information (PI) that is unavailable to others. Examples include task preferences, objectives, and beliefs. Here, we focus on the human-robot dyadic scenarios where the human has private information, but is unable to directly convey it to the robot. We present Q-Network with Private Information and Cooperation (Q-PICo), a method for training robots that can interactively assist humans with PI. In contrast to existing approaches, we explicitly model PI prediction, leading to a more interpretable network architecture. We also contribute Juiced, an environment inspired by the popular video gameOvercooked, to test Q-PICo and other related methods for human-robot collaboration. Our initial experiments in Juiced show that the agents trained with Q-PICo can accurately predict PI and exhibit collaborative behavior.

## Resources

You can find our paper here. Check out our repository here on github

## Citation

Please consider citing our paper if you build upon our results and ideas.

Eugene Lim★, Bing Cai Kok★, Songli Wang★, Joshua Lee★, Harold Soh★, “Juiced and Ready to Predict Private Information in Deep Cooperative Reinforcement Learning”, ACM/IEEE International Conference on Human-Robot Interaction (HRI)

@article{lim2020juiced,
title={Juiced and Ready to Predict Private Information in Deep Cooperative Reinforcement Learning},
author={Lim, Eugene and Kok, Bing Cai and Wang, Songli and Lee, Joshua and Soh, Harold},
journal={ACM/IEEE International Conference on Human-Robot Interaction (HRI)},
pages = {343–345},
year = {2020},
doi = {10.1145/3371382.3378308},
publisher={Association for Computing Machinery}}