David Barker

Reinforcement Learning · with McFall · 2013

A researcher in the Science corpus: 1 paper; trusts 6 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom David Barker trusts overall

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

  1. Richard S. Sutton
  2. Stephen W. Carden
  3. N. Schraudolph
  4. Andrew Barto

Papers from David Barker's perspective

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

  1. Reinforcement Learning: An Introduction
  2. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning
  3. Planning and Acting in Partially Observable Stochastic Domains
  4. Markov Games as a Framework for Multi-Agent Reinforcement Learning
  5. Adapting Bias by Gradient Descent: An Incremental Version of Delta-Bar-Delta
  6. An Introduction to Reinforcement Learning
  7. Learning to predict by the methods of temporal differences
  8. Local Gain Adaptation in Stochastic Gradient Descent
  9. Distributed dynamic programming
  10. Web-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising in Microsoft's Bing Search Engine

Their papers