An implementation of a variational auto-encoder with latent state predictions of non-linear dynamics
The results are relatable to the paper A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning (see repo https://github.com/simonkamronn/kvae). The main difference is that here an LSTM is used instead of a Kalman Filter. Moreover, the paper discuses other functionality such as data imputation. This repo instead focuses only on predicting.
Before running main.py make sure to generate the training data by running either of kvae_data/box.py, kvae_data/box_gravity.py, kvae_data/polygon.py.
pytorchpygamepymunkmatplotlibnumpytqdm