Ubbo Visser

Reinforcement Learning · 2001–2015

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 Ubbo Visser trusts overall

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

  1. Richard S. Sutton
  2. Hamid Reza Maei
  3. Thomas Degris
  4. Matthew E. Taylor
  5. Andrew Barto
  6. Harm van Seijen

Papers from Ubbo Visser's perspective

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

  1. Reinforcement Learning: An Introduction
  2. Gradient temporal-difference learning algorithms
  3. GQ(lambda): A general gradient algorithm for temporal-difference prediction learning with eligibility traces
  4. Toward Off-Policy Learning Control with Function Approximation
  5. Horde: a scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction
  6. Off‐Policy Actor‐Criticアルゴリズムによる強化学習
  7. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
  8. Value Function Approximation in Reinforcement Learning Using the Fourier Basis
  9. Reinforcement learning in robotics: A survey
  10. Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding

Their papers