(Inverse) optimal control for linear quadratic Gaussian systems
Project description
Inverse optimal control for continuous psychophysics
This repository contains the official JAX implementation of the inverse optimal control method presented in the paper:
Installation
The package can be installed via pip
python -m pip install lqg
although I recommend cloning the repository to get the most recent version and installing locally with a virtual environment
python -m venv env
source env/bin/activate
python -m pip install -e .
Usage examples
-
main.py
shows how to simulate data and infer parameters using the LQG model of the tracking task. -
notebooks/01-HowTo.ipynb
explains the model and its parameters in more detail, including the extension to subjective internal models. -
notebooks/02-Data.ipynb
fits the ideal observer and bounded actor model to the data from Bonnen et al. (2015) to reproduce Fig. 4A from our paper.
Citation
If you use our method or code in your research, please cite our paper:
@article{straub2022putting,
title={Putting perception into action with inverse optimal control for continuous psychophysics},
author={Straub, Dominik and Rothkopf, Constantin A},
journal={eLife},
volume={11},
pages={e76635},
year={2022},
publisher={eLife Sciences Publications Limited}
}
Signal-dependent noise
This implementation supports the basic LQG framework. For the extension to signal-dependent noise (Todorov, 2005), please see our NeurIPS 2021 paper and its implementation.
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