Learning and Policy Search in Stochastic Dynamical Systems with Bayesian   Neural Networks
  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. 
Stefan Depeweg, Jos\'e Miguel Hern\'andez-Lobato, Finale Doshi-Velez,   Steffen Udluft
stat.ML cs.LG