  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
$\alpha$-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.
