Bálint Takács

Reinforcement Learning · 2002

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Bálint Takács trusts overall

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

  1. Richard S. Sutton
  2. Kenji Doya
  3. Sridhar Mahadevan
  4. Ryuichi Oka
  5. Michael L. Littman
  6. Csaba Szepesv Ari
  7. Andrew Barto
  8. Ben J. A. Kröse
  9. John N. Tsitsiklis

Papers from Bálint Takács's perspective

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

  1. Reinforcement Learning: An Introduction
  2. Learning from delayed rewards
  3. Feudal Reinforcement Learning
  4. On the Convergence of Stochastic Iterative Dynamic Programming Algorithms
  5. Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
  6. Markov Games as a Framework for Multi-Agent Reinforcement Learning
  7. Asynchronous Stochastic Approximation and Q-Learning
  8. Dynamic Programming
  9. Automatic Programming of Behavior-Based Robots Using Reinforcement Learning
  10. Reinforcement Learning in Continuous Time and Space

Their papers