https://github.com/locuslab/differentiable-mpc

https://github.com/locuslab/differentiable-mpc

Last synced: 2 days ago
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• Host: GitHub
• URL: https://github.com/locuslab/differentiable-mpc
• Owner: locuslab
• License: mit
• Created: 2018-10-26T14:49:49.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-02-08T19:48:51.000Z (almost 2 years ago)
• Last Synced: 2024-12-25T13:13:21.081Z (9 days ago)
• Language: Python
• Size: 5.52 MB
• Stars: 251
• Watchers: 10
• Forks: 52
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.mit

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README

        
# Differentiable MPC for End-to-end Planning and Control

This repository is by [Brandon Amos](http://bamos.github.io),

Ivan Dario Jimenez Rodriguez, Jacob Sacks, Byron Boots,

and [J. Zico Kolter](http://zicokolter.com)

and contains the [PyTorch](https://pytorch.org) source code to

reproduce the experiments in our NIPS 2018 paper

[Differentiable MPC for End-to-end Planning and Control](https://arxiv.org/abs/1810.13400).

The PyTorch implementation of the fast and differentiable MPC solver

we developed for this work is available as a standalone library at

[locuslab/mpc.pytorch](https://locuslab.github.io/mpc.pytorch/).

If you find this repository helpful in your publications,

please consider citing our paper.

```

@article{amos2018differentiable,

  title={{Differentiable MPC for End-to-end Planning and Control}},

  author={Brandon Amos and Ivan Jimenez and Jacob Sacks and Byron Boots and J. Zico Kolter},

  booktitle={{Advances in Neural Information Processing Systems}},

  year={2018}

}

```

## Setup and Dependencies

+ Python/numpy/[PyTorch](https://pytorch.org)

+ [locuslab/mpc.pytorch](https://github.com/locuslab/mpc.pytorch)

# LQR Imitation Learning Experiments

From within the `imitation_lqr` directory:

1. `train.py` is the main training script for the experiment 

   in Section 5.3.

# Non-Convex Imitation Learning Experiments

From within the `imitation_nonconvex` directory:

1. `make_dataset.py` should be run to create a dataset of trajectories

   for each environment.

2. `il_exp.py` is the main training script for each experiment.

3. `run-pendulum-cartpole.sh` runs all of the experiments for the

   pendulum and cartpole environments in Section 5.3.

3. `run-complex-pendulum.sh` runs all of the experiments for the

   non-realizable pendulum environment in Section 5.4.