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

Stefan Depeweg; José Miguel Hernández-Lobato; Finale Doshi-Velez; Steffen Udluft

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arXiv · 1605.07127

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

Stefan Depeweg·José Miguel Hernández-Lobato·Finale Doshi-Velez·Steffen Udluft

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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.

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BibTeX RIS

Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft. 2016-05-23. Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks. https://arxiv.org/abs/1605.07127

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