Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks

Citation:

Depewag S, Hernández-Lobato JM, Doshi-Velez F, Udluft S. Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. arXiv:1605.07127. 2016.
Paper1.65 MB

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  -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.