%0 Journal Article %J ICLR %D 2017 %T Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks %A Stefan Depewag %A José Miguel Hernández-Lobato %A Finale Doshi-Velez %A Steffen Udluft %X

We present an algorithm for model-based reinforcement learning that combines Bayesian neural networks (BNNs) with random roll-outs and stochastic optimization for policy learning. The BNNs are trained by minimizing α  -divergences, allowing us to capture complicated statistical patterns in the transition dynamics, e.g. multi-modality and heteroskedasticity, which are usually missed by other common modeling approaches. We illustrate the performance of our method by solving a challenging benchmark where model-based approaches usually fail and by obtaining promising results in a real-world scenario for controlling a gas turbine.

%B ICLR %G eng