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https://github.com/thiagopbueno/tf-mpc
An implementation of model-predictive control algorithms using TensorFlow 2
https://github.com/thiagopbueno/tf-mpc
ilqr lqr model-predictive-control mpc tensorflow2
Last synced: about 2 months ago
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An implementation of model-predictive control algorithms using TensorFlow 2
- Host: GitHub
- URL: https://github.com/thiagopbueno/tf-mpc
- Owner: thiagopbueno
- License: gpl-3.0
- Created: 2020-02-19T18:20:45.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2021-05-03T23:18:25.000Z (over 3 years ago)
- Last Synced: 2024-09-25T16:05:06.895Z (3 months ago)
- Topics: ilqr, lqr, model-predictive-control, mpc, tensorflow2
- Language: Python
- Size: 216 KB
- Stars: 18
- Watchers: 0
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-tensorflow-2 - An implementation of model-predictive control algorithms using TensorFlow 2
README
# tf-mpc [![Py Versions][py-versions.svg]][pypi-project] [![PyPI version][pypi-version.svg]][pypi-version] [![Build Status][travis.svg]][travis-project] [![License: GPL v3][license.svg]][license]
# Quickstart
**tfmpc** is a Python3.6+ package available in PyPI.
```text
$ pip3 install -U tfmpc
```
# Usage
```bash
$ tfmpc ilqr --help
Usage: tfmpc ilqr [OPTIONS] ENV
Run iLQR for a given environment and horizon.
Args:
ENV: Path to the environment's config JSON file.
Options:
--online Online mode flag. [default: False]
--ignore-final-cost Ignore state-dependent final cost.
[default: False]
-hr, --horizon INTEGER RANGE The number of timesteps. [default: 10]
--atol FLOAT RANGE Absolute tolerance for convergence.
[default: 0.005]
-miter, --max-iterations INTEGER RANGE
Maximum number of iterations. [default:
100]
--logdir PATH Directory used for logging results.
[default: /tmp/ilqr/]
-ns, --num-samples INTEGER RANGE
Number of runs. [default: 1]
-nw, --num-workers INTEGER RANGE
Number of worker processes (min=1, max=12).
[default: 1]
-v, --verbose Verbosity level flag.
--help Show this message and exit.
```
# Examples
## LQR
```bash
$ python examples/lqr.py
Trajectory(init=[-0.9436722 -5.9413767 -9.7090645], final=[-6.831274 3.5397437 0.79844564], total=-34.2876)
Steps | States | Actions | Costs
===== | ============================== | ============================== | ========
0 | [-29.6400, 12.4868, -6.1247] | [ 12.0202, 6.2650, 2.7019] | 9.9491
1 | [ 1.1229, -1.0781, -0.9041] | [ 24.8006, 16.6294, -10.9740] | 49.6677
2 | [ -8.8750, 2.3962, -4.4266] | [ 3.7858, 3.3769, -1.8138] | -1.6455
3 | [ -9.3617, 3.2755, -3.5806] | [ 11.8333, 7.8142, -3.6503] | -11.4392
4 | [ -6.6389, 2.0026, -3.2240] | [ 11.3348, 7.6663, -4.2552] | -11.8703
5 | [ -7.7849, 2.3658, -3.6332] | [ 9.6319, 6.4642, -3.2991] | -12.2632
6 | [ -7.5215, 2.4822, -3.0080] | [ 10.1523, 6.7136, -3.4948] | -12.7255
7 | [ -6.2336, 1.5849, -2.9592] | [ 9.6488, 6.2573, -3.1976] | -12.8830
8 | [ -8.7144, 2.0473, -4.4850] | [ 10.1518, 6.4578, -2.9710] | -11.6011
9 | [ -6.8313, 3.5397, 0.7984] | [ 8.3644, 5.6785, -3.5642] | -12.9032
```
## Linear Navigation
```bash
$ python examples/navigation_lqr.py
Goal = [[-17.498825073242188], [-55.275390625]]
Trajectory(init=[0. 0.], final=[-17.498783 -55.275257], total=-32385.3555)
Steps | States | Actions | Costs
===== | ==================== | ==================== | ==========
0 | [-12.8100, -40.4644] | [-12.8100, -40.4644] | 900.7320
1 | [-16.2425, -51.3068] | [ -3.4324, -10.8424] | -3055.5571
2 | [-17.1622, -54.2120] | [ -0.9197, -2.9052] | -3339.6064
3 | [-17.4086, -54.9905] | [ -0.2464, -0.7784] | -3360.0002
4 | [-17.4747, -55.1990] | [ -0.0660, -0.2086] | -3361.4644
5 | [-17.4924, -55.2549] | [ -0.0177, -0.0559] | -3361.5696
6 | [-17.4971, -55.2699] | [ -0.0047, -0.0150] | -3361.5774
7 | [-17.4984, -55.2739] | [ -0.0013, -0.0040] | -3361.5776
8 | [-17.4987, -55.2750] | [ -0.0003, -0.0011] | -3361.5774
9 | [-17.4988, -55.2753] | [ -0.0001, -0.0003] | -3361.5776
```
# Documentation
Please refer to [https://tfmpc.readthedocs.io/](https://tfmpc.readthedocs.io/) for the code documentation.
# License
Copyright (c) 2020- Thiago P. Bueno All Rights Reserved.
tfmpc is free software: you can redistribute it and/or modify it
under the terms of the GNU Lesser General Public License as published by
the Free Software Foundation, either version 3 of the License, or (at
your option) any later version.
tfmpc is distributed in the hope that it will be useful, but
WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Lesser
General Public License for more details.
You should have received a copy of the GNU Lesser General Public License
along with tfmpc. If not, see http://www.gnu.org/licenses/.
[py-versions.svg]: https://img.shields.io/pypi/pyversions/tfmpc.svg?logo=python&logoColor=white
[pypi-project]: https://pypi.org/project/tfmpc
[pypi-version.svg]: https://badge.fury.io/py/tfmpc.svg
[pypi-version]: https://badge.fury.io/py/tfmpc
[travis.svg]: https://img.shields.io/travis/thiagopbueno/tf-mpc/master.svg?logo=travis
[travis-project]: https://travis-ci.org/thiagopbueno/tf-mpc
[license.svg]: https://img.shields.io/badge/License-GPL%20v3-blue.svg
[license]: https://github.com/thiagopbueno/tf-mpc/blob/master/LICENSE