Switching Linear Dynamics for Variational Bayes Filtering

Understand

System identification of complex and nonlinear systems is a central problem for model predictive control and model-based reinforcement learning.

• Despite their complexity, such systems can often be approximated well by a set of linear dynamical systems if broken into appropriate subsequences.
• This mechanism not only helps us find good approximations of dynamics, but also gives us deeper insight into the underlying system.
• Leveraging Bayesian inference, Variational Autoencoders and Concrete relaxations, we show how to learn a richer and more meaningful state space, e.g.