variational-inference

We present a model-based RL algorithm that constrains training to a single dynamic mode with high probability. This is a difficult problem because the mode constraint is a hidden variable associated with the environment’s dynamics. As such, it is 1) unknown a priori and 2) we do not observe its output from the environment, so cannot learn it with supervised learning.

Mode remaining navigation (and exploration) in unknown multimodal dynamical systems via model-based reinforcement learning.

Mixture models are inherently unidentifiable as different combinations of component distributions and mixture weights can generate the …

This work presents a two-stage method to perform trajectory optimisation in multimodal dynamical systems with unknown nonlinear stochastic transition dynamics. The method finds trajectories that remain in a preferred dynamics mode where possible and in regions of the transition dynamics model that have been observed and can be predicted confidently.

This work introduces a variational lower bound for the Mixture of Gaussian Process Experts model with a GP-based gating network based on sparse GPs. The model (and inference) are implemented in GPflow/TensorFlow.

This work implements and compares a variety of approximate inference techniques for the tasks of image de-noising (restoration) and image segmentation.