@article {453946,
	title = {Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks},
	journal = {ICLR},
	year = {2017},
	abstract = {
	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\&nbsp; -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.

},
	author = {Stefan Depewag and Jos{\'e} Miguel Hern{\'a}ndez-Lobato and Finale Doshi-Velez and Steffen Udluft}
}
