This repository is inteded to provide reference implementations for the work in the paper Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal. If you make use of this code, please cite our preprint:

@misc{mu2022rirl,
     title={Solving Dynamic Principal-Agent Problems with a Rationally Inattentive Principal}, 
     author={Tong Mu and Stephan Zheng and Alexander Trott},
     year={2022},
     eprint={2202.01691},
     archivePrefix={arXiv},
     primaryClass={cs.MA}
}

The main technical contribution of this work, the RIRL-actor policy class supporting temporally extended settings with heterogeneous information channels, is implemented in multi_channel_RI.py. Other supporting implementations of trainable modules are organized within the agents/ directory.

This repository includes reference training pipelines for each of the two experimental settings:

• Payment schedule optimization in a single-step ("bandit") setting.
• Wage optimization in a multi-step, multi-Agent, multi-channel setting.

These reference pipelines are organized into jupyter notebooks:

• PA_bandit_game/PA_Bandit-training_pipeline_reference.ipynb (payment optimization)
• PA_multiagent_game/multiagent_signaling-training_pipeline_reference.ipynb (wage optimization)

Each notebook is structured to exemplify a single training run. Static hyperparameters are defined in associated config YAML files. Important experimental variables (such as mutual information costs) are set within the notebooks. In this way, the notebooks can be run repeatedly to manually perform sweeps over variables of interest.