Katja Verbeeck

Reinforcement Learning · 2006–2008

A researcher in the Science corpus: 2 papers; trusts 2 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

Whom Katja Verbeeck trusts overall

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

  1. Richard S. Sutton
  2. Andrew Barto
  3. Craig Boutilier

Who trusts Katja Verbeeck overall

  1. Ann Nowé
  2. Yann-Michaël De Hauwere

Papers from Katja Verbeeck's perspective

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

  1. Reinforcement Learning: An Introduction
  2. An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems
  3. Reinforcement Learning to Play an Optimal Nash Equilibrium in Team Markov Games
  4. Planning, Learning and Coordination in Multiagent Decision Processes
  5. A Course in Game Theory
  6. Nash Q-Learning for General-Sum Stochastic Games
  7. All learning is Local: Multi-agent Learning in Global Reward Games
  8. Markov Games as a Framework for Multi-Agent Reinforcement Learning
  9. Residual Algorithms: Reinforcement Learning with Function Approximation
  10. Markov Decision Processes: Discrete Stochastic Dynamic Programming

Their papers