Nathan Umbanhowar

Reinforcement Learning · 2020

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Nathan Umbanhowar trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Richard S. Sutton
  2. Doina Precup
  3. Andrew Barto
  4. Martin L. Puterman
  5. Satinder Singh
  6. Hado van Hasselt
  7. Lihong Li
  8. Nan Jiang
  9. Richard Bellman
  10. Michael L. Littman

Papers from Nathan Umbanhowar's perspective

Ordered by how strongly the researchers Nathan Umbanhowar trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Reinforcement Learning: An Introduction
  2. ImageNet classification with deep convolutional neural networks
  3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  4. Gradient-based learning applied to document recognition
  5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
  6. Long Short-Term Memory
  7. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning
  8. Markov Decision Processes: Discrete Stochastic Dynamic Programming
  9. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
  10. Attention is All you Need (Neural Information Processing Systems)

Their papers