Learning Stable Deep Dynamics Models

Companion code to "Learning Stable Deep Dynamics Models" (Manek and Kolter, 2019)

Installation

You need Python 3.6 or later, with packages listed in requirements.txt. Ensure that the directories experiments/ and runs/ are writable.

Running

All training commands produce output in the experiments/ folder, automatically named with the hyperparameters used in training. You can track training progress using tensorboard runs/.

Pendulum

To train the models for an <n>-link pendulum, you should first run the command:

./train_pendulum_simple <n>

which will create and cache the training data and the evaluation data. (Warning: this is a cpu-heavy task that may take hours to complete.) Once that is complete, you can train multiple models in parallel with:

./train_many <n>

You can concurrently train train models with different <n>.

Ensure that pendulum-cache/ is writable.

VAE

To train the VAE, you need to convert videos to sequentially-numbered frames in youtube/<video>/fr_[0-9]*.jpg. A good place to start is this video, which you can download and convert with:

mkdir youtube
cd youtube
youtube-dl -o bonfire.%(ext) -f 134 I4dYM1biVq0
ffmpeg -ss 0:01:00 -i bonfire.mp4 -t 3:20 -vf scale=320:240 bonfire/fr_%06d.png

(You need to install youtube-dl and ffmpeg.)

Once that is done, you can train the simple VAE model with:

./train_vae_simple bonfire

And the stable VAE with:

./train_vae_stable stable_exp bonfire 0.0005 -0.25 0.00 PSICNN 0.001