William H. Guss | DeepAI

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∙ 01/26/2021

The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors

Although deep reinforcement learning has led to breakthroughs in many di...

11 William H. Guss, et al. ∙

∙ 03/10/2020

The MineRL Competition on Sample-Efficient Reinforcement Learning Using Human Priors: A Retrospective

To facilitate research in the direction of sample-efficient reinforcemen...

9 Stephanie Milani, et al. ∙

∙ 03/10/2020

Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning

To facilitate research in the direction of sample-efficient reinforcemen...

0 Stephanie Milani, et al. ∙

∙ 10/03/2019

On Universal Approximation by Neural Networks with Uniform Guarantees on Approximation of Infinite Dimensional Maps

The study of universal approximation of arbitrary functions f: X→Y by ne...

12 William H. Guss, et al. ∙

∙ 07/29/2019

MineRL: A Large-Scale Dataset of Minecraft Demonstrations

The sample inefficiency of standard deep reinforcement learning methods ...

7 William H. Guss, et al. ∙

∙ 04/22/2019

The MineRL Competition on Sample Efficient Reinforcement Learning using Human Priors

Though deep reinforcement learning has led to breakthroughs in many diff...

10 William H. Guss, et al. ∙

∙ 02/13/2018

On Characterizing the Capacity of Neural Networks using Algebraic Topology

The learnability of different neural architectures can be characterized ...

0 William H. Guss, et al. ∙

∙ 12/14/2016

Deep Function Machines: Generalized Neural Networks for Topological Layer Expression

In this paper we propose a generalization of deep neural networks called...

0 William H. Guss, et al. ∙

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