https://github.com/pfnet-research/chainer-differentiable-mpc
Differentiable MPC in Chainer, developed as part of PFN summer internship 2019.
https://github.com/pfnet-research/chainer-differentiable-mpc
chainer deep-learning lqr machine-learning model-predictive-control mpc optimal-control trajectory-optimization
Last synced: 3 months ago
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Differentiable MPC in Chainer, developed as part of PFN summer internship 2019.
- Host: GitHub
- URL: https://github.com/pfnet-research/chainer-differentiable-mpc
- Owner: pfnet-research
- License: mit
- Created: 2019-10-07T02:55:48.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-08-23T18:05:03.000Z (almost 4 years ago)
- Last Synced: 2025-10-04T23:34:31.574Z (8 months ago)
- Topics: chainer, deep-learning, lqr, machine-learning, model-predictive-control, mpc, optimal-control, trajectory-optimization
- Language: Python
- Size: 515 KB
- Stars: 15
- Watchers: 16
- Forks: 6
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Differentiable MPC in Chainer
Attempt to port [mpc.pytorch](https://github.com/locuslab/mpc.pytorch) to [Chainer](https://chainer.org).
**Disclaimer: PFN provides no warranty or support for this software. Use it at your own risk.**
This software is developed as part of [PFN summer internship 2019](https://preferred.jp/en/news/internship2019/) and the main developer is Shun Arahata.
## Overview of Differentiable MPC
## Install
```
$ pipenv install
```
## Dependencies
This code has been tested using libraries below:
* numpy = "1.17.2"
* chainer = "6.3.0"
* jupyter = "1.0.0"
* matplotlib = "3.1.1"
* jupytext = "1.2.4"
* scipy = "1.3.1"
* pathlib = "1.0.1"
* torch = "1.2.0"
See also `Pipfile`.
## Usage
```
$ pipenv run python env_dx/make_dataset.py
$ pipenv run python env_dx/il_exp.py
```
## Example Result
## References
* [1] B. Amos, I. Jimenez, J. Sacks, B. Boots, and Z. Kolter. “Differentiable MPC for End-to-end Planning and Control”
In NIPS'18 Proceedings of Advances in Neural Information Processing Systems.
Available: https://arxiv.org/abs/1810.13400 . Experiment code: https://github.com/locuslab/differentiable-mpc
* [2] Yuval Tassa, Nicolas Mansard and Emo Todorov. “Control-Limited Differential Dynamic Programming” ICRA'14 IEEE International Conference on Robotics and Automation. Available: https://homes.cs.washington.edu/~todorov/papers/TassaICRA14.pdf . Code: https://jp.mathworks.com/matlabcentral/fileexchange/52069-ilqg-ddp-trajectory-optimization
* [3] Brandon Amos, and J. Zico Kolter, “OptNet: Differentiable Optimization as a Layer in Neural Networks” In ICML'17 Proceedings of the 34th International Conference on Machine Learning. Available: https://arxiv.org/abs/1703.00443 . Code: https://github.com/locuslab/qpth/ . Chainer-port: https://github.com/msakai/chainer-optnet
* [4] Yuval Tassa, Tom Erez and Emanuel Todorov. “Synthesis and Stabilization of Complex Behaviors through
Online Trajectory Optimization” IROS'12 IEEE/RSJ International Conference on Intelligent Robots and Systems. Available: https://homes.cs.washington.edu/~todorov/papers/TassaIROS12.pdf .
## Useful Links
* [RL — LQR & iLQR Linear Quadratic Regulator](https://medium.com/@jonathan_hui/rl-lqr-ilqr-linear-quadratic-regulator-a5de5104c750)
* [EE363: Lecture Slides (Lecture by Stephen P. Boyd)](https://stanford.edu/class/ee363/lectures.html)
* [Optimal Control and
Planning, CS 294-112: Deep Reinforcement Learning
Sergey Levine](http://rail.eecs.berkeley.edu/deeprlcourse-fa17/f17docs/lecture_8_model_based_planning.pdf)
* [Synthesis and Stabilization of Complex Behaviors through Online Trajectory Optimization](https://speakerdeck.com/yuishihara/synthesis-and-stabilization-of-complex-behaviors-through-online-trajectory-optimization) [in Japanese]
* [Differential Dynamic Programming(DDP)/iterative LQR(iLQR)/Sequential LQR(SLQ)](http://blog.syundo.org/post/20180917-ddp-ilqr-slq/) [in Japanese]
## LICENSE
MIT License (see the [LICENSE](LICENSE) file for details).
Note: Part of the code is derived from [mpc.pytorch](https://github.com/locuslab/mpc.pytorch) and [differentiable-mpc](https://github.com/locuslab/differentiable-mpc/) which are also licensed under MIT License (see the [LICENSE_THIRD_PARTY](LICENSE_THIRD_PARTY) file for details).
Also this package still uses [PyTorch](https://pytorch.org/) for fast batch LU factorization (`torch.lu`) and solving (`torch.lu_solve`).