CUED Publications database

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

Depeweg, S and Hernández-Lobato, JM and Doshi-Velez, F and Udluft, S Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. (Unpublished)

Full text not available from this repository.

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 $\alpha$-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.

Item Type: Article
Uncontrolled Keywords: stat.ML stat.ML cs.LG
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 20:06
Last Modified: 18 Jul 2017 08:50
DOI: