title: Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
abstract: We present an algorithm for policy search in stochastic dynamical systems using
  model-based reinforcement learning. The system dynamics are described with
  Bayesian neural networks (BNNs) that include stochastic input variables.  These
  input variables allow us to capture complex statistical
  patterns in the transition dynamics (e.g. multi-modality and
  heteroskedasticity), which are usually missed by alternative modeling approaches. After
  learning the dynamics, our BNNs are then fed into an algorithm that performs
  random roll-outs and uses stochastic optimization for policy learning. We train
  our BNNs by minimizing $\alpha$-divergences with $\alpha = 0.5$, which usually produces better
  results than other techniques such as variational Bayes. We illustrate the performance of our method by
  solving a challenging problem where model-based approaches usually fail and by
  obtaining promising results in real-world scenarios including the control of a
  gas turbine and an industrial benchmark.
authors: Stefan Depeweg José Miguel Hernández-Lobato Finale Doshi-Velez Steffen Udluft
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