LQG: 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
Since publication of our eLife paper, I have substantially updated the package. If you want to use the package as described in the paper, please install an older version <0.2.0:
python -m pip install lqg==0.1.9
If you want the latest development version, I recommend cloning the repository and installing locally in a virtual environment:
git clone git@github.com:dominikstrb/lqg.git
cd lqg
python -m venv env
source env/bin/activate
python -m pip install -e .
Usage examples
The notebooks in the documentation illustrate how to use the lqg package to define optimal control models, simulate trajectories, and infer parameters from observed data.
Overviewexplains the model and its parameters in more detail, including the extension to subjective internal models (based on my tutorial at CCN 2022)Dataapplies the method to data from a tracking experiment
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}
}
Extensions
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.
Non-linear dynamics
We have recently extended this approach to non-linear dynamics and non-quadratic costs. Please check out our NeurIPS 2023 paper and its implementation.
1 maintainer
Metadata
1 maintainer
Release files for lqg 0.3.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| lqg-0.3.1.tar.gz | 19.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| lqg-0.3.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 46.6 kB
Release files / lqg-0.3.1.tar.gz
| Download URL | lqg-0.3.1.tar.gz |
|---|---|
| Size | 19.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
3e26498cc1f97fcf5d135a494b212108cd0f6e32cc967d43d7d110e1c91c0769
|
|
BLAKE2b-256 checksum How to use checksums |
39b39ff5977e3e9ab43afbfdf9b5ea1b9fa73a17f98f7c087a9bb8bd85f7ba28
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.12.19 {"installer":{"name":"uv","version":"0.12.19","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
|
Release files / lqg-0.3.1-py3-none-any.whl
| Download URL | lqg-0.3.1-py3-none-any.whl |
|---|---|
| Size | 26.7 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
ead3941980581df20bd75e6bc54ade78a90c8b3cef2d0e1cdb096cd77a134b27
|
|
BLAKE2b-256 checksum How to use checksums |
34c62d33335ab36965ac45f522dc81c6fe39bb6b2a049729f7bf10b94f5b9e56
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
uv/0.12.19 {"installer":{"name":"uv","version":"0.12.19","subcommand":["publish"]},"python":null,"implementation":{"name":null,"version":null},"distro":{"name":"macOS","version":null,"id":null,"libc":null},"system":{"name":null,"release":null},"cpu":null,"openssl_version":null,"setuptools_version":null,"rustc_version":null,"ci":null}
|
