Bayesian Neural Networks with Random Inputs for Model Based Reinforcement Learning

I describe here our recent ICLR paper [1] [code] [talk], which introduces a novel method for model-based reinforcement learning. The main author of this work is Stefan Depeweg, a phd student at Technical University in Munich who I am co-supervising. The key contribution is in our models: Bayesian neural networks with random inputs, whose input layer contains both input features but also random variables which are propagated forward through the network and transformed into an arbitrary noise signal at the output layer. The random inputs enable our models to automatically capture complex noise patterns, improving the quality of our model-based simulations and producing better policies in practice. We address the problem of policy search in stochastic dynamical systems.