Amy Greenwald

Reinforcement Learning · 2003–2005

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Amy Greenwald trusts overall

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

  1. Michael L. Littman
  2. Junling Hu
  3. Richard S. Sutton
  4. Michael H. Bowling
  5. Yoav Freund
  6. Martin L. Puterman
  7. Andrew Barto
  8. Ryuichi Oka
  9. Michael P. Wellman
  10. Manuela M. Veloso

Who trusts Amy Greenwald overall

  1. Michael H. Bowling
  2. Matthew J. Hausknecht
  3. Liviu Panait
  4. Yoav Shoham
  5. Ann Nowé
  6. Ermo Wei
  7. Jacob William Crandall
  8. Lucian Busoniu
  9. Enrique Munoz de Cote
  10. Sean Luke

Papers from Amy Greenwald's perspective

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

  1. Reinforcement Learning: An Introduction
  2. Markov Games as a Framework for Multi-Agent Reinforcement Learning
  3. Markov Decision Processes: Discrete Stochastic Dynamic Programming
  4. ImageNet classification with deep convolutional neural networks
  5. Gradient-based learning applied to document recognition
  6. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
  7. Correlated Q-Learning
  8. Asynchronous Methods for Deep Reinforcement Learning
  9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  10. Residual Algorithms: Reinforcement Learning with Function Approximation

Their papers