  Differentiable particle filters are an emerging class of models that combine
sequential Monte Carlo techniques with the flexibility of neural networks to
perform state space inference. This paper concerns the case where the system
may switch between a finite set of state-space models, i.e. regimes. No prior
approaches effectively learn both the individual regimes and the switching
process simultaneously. In this paper, we propose the neural network based
regime learning differentiable particle filter (RLPF) to address this problem.
We further design a training procedure for the RLPF and other related
algorithms. We demonstrate competitive performance compared to the previous
state-of-the-art algorithms on a pair of numerical experiments.
