Efficient Exploration in Model-free Reinforcement Learning — I describe here our recent NeurIPS paper [1] [code], which introduces Successor Uncertainties (SU), a state-of-the-art method for efficient exploration in model-free reinforcement learning. The leading authors are David Janz and Jiri Hron, two PhD students from the Cambridge Machine Learning group, with the work having originated during an internship…