Roberto Pieraccini

Reinforcement Learning · 1997–2009

A researcher in the Science corpus: 5 papers; trusts 9 researchers and is trusted by 36 researchers here, as inferred from the citation graph.

Subfields

Whom Roberto Pieraccini trusts overall

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

  1. Leslie Pack Kaelbling
  2. Andrew Barto
  3. Richard S. Sutton
  4. Michael L. Littman
  5. Andrew Y. Ng
  6. Andrew W. Moore
  7. Satinder Singh
  8. G. Tesauro
  9. Steven J. Bradtke

Who trusts Roberto Pieraccini overall

  1. Iulian Serban
  2. Jiwei Li
  3. Susannah Young
  4. Jianfeng Gao
  5. Milica Gasic
  6. Zachary Chase Lipton
  7. Johannes Pittermann
  8. Jason Williams
  9. Oliver Lemon
  10. Michel Galley

Papers from Roberto Pieraccini's perspective

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

  1. Reinforcement Learning: An Introduction
  2. Algorithms for Inverse Reinforcement Learning
  3. Planning and Acting in Partially Observable Stochastic Domains
  4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  5. Gradient-based learning applied to document recognition
  6. ImageNet classification with deep convolutional neural networks
  7. Learning to Act Using Real-Time Dynamic Programming
  8. TD-Gammon, a Self-Teaching Backgammon Program, Achieves Master-Level Play
  9. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
  10. Long Short-Term Memory

Their papers