Adam Laud

Reinforcement Learning · 2004

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Adam Laud trusts overall

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

  1. G. Tesauro
  2. Richard S. Sutton
  3. Andrew Barto
  4. Maja J. Mataric
  5. Michael Kearns
  6. Satinder Singh

Who trusts Adam Laud overall

  1. Ana Cecilia Tenorio-González
  2. Guillaume Vaudaux-Ruth
  3. Eduardo F. Morales
  4. Adrien Chan-Hon-Tong
  5. Niels Justesen
  6. Luis Villaseñor-Pineda
  7. Catherine Achard
  8. Jens Kober
  9. Sampo Kuutti

Papers from Adam Laud's perspective

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

  1. Practical Issues in Temporal Difference Learning
  2. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
  3. Long Short-Term Memory
  4. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning
  5. Continuous control with deep reinforcement learning (International Conference on Learning Representations)
  6. Learning internal representations by error propagation
  7. Reinforcement Learning: An Introduction
  8. Markov Decision Processes: Discrete Stochastic Dynamic Programming
  9. Asynchronous Methods for Deep Reinforcement Learning
  10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers