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cs.LG2019★ 4 cited
On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps
William H. Guss, Ruslan Salakhutdinov
The study of universal approximation of arbitrary functions by neural networks has a rich and thorough history dating back to Kolmogorov (1957). In…
cs.LG2019
MineRL: A Large-Scale Dataset of Minecraft Demonstrations
William H. Guss, Brandon Houghton, Nicholay Topin +4
The sample inefficiency of standard deep reinforcement learning methods precludes their application to many real-world problems. Methods which leverage human demonstrations require…
cs.LG2019
The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors
William H. Guss, Cayden Codel, Katja Hofmann +9
Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinf…