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Istvan Szita: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Istvan Szita, Bálint Takács, András Lörincz
    Reinforcement Learning Integrated with a Non-Markovian Controller. [Citation Graph (0, 0)][DBLP]
    ECAI, 2002, pp:365-369 [Conf]
  2. Istvan Szita, Viktor Gyenes, András Lörincz
    Reinforcement Learning with Echo State Networks. [Citation Graph (0, 0)][DBLP]
    ICANN (1), 2006, pp:830-839 [Conf]
  3. Istvan Szita, Bálint Takács, András Lörincz
    Searching for Plannable Domains can Speed up Reinforcement Learning [Citation Graph (0, 0)][DBLP]
    CoRR, 2002, v:0, n:, pp:- [Journal]
  4. Bálint Takács, Istvan Szita, András Lörincz
    Temporal plannability by variance of the episode length [Citation Graph (0, 0)][DBLP]
    CoRR, 2003, v:0, n:, pp:- [Journal]
  5. Istvan Szita, András Lörincz
    Applying Policy Iteration for Training Recurrent Neural Networks [Citation Graph (0, 0)][DBLP]
    CoRR, 2004, v:0, n:, pp:- [Journal]
  6. Istvan Szita, András Lörincz
    Kalman filter control in the reinforcement learning framework [Citation Graph (0, 0)][DBLP]
    CoRR, 2003, v:0, n:, pp:- [Journal]
  7. Istvan Szita, András Lörincz
    Reinforcement Learning with Linear Function Approximation and LQ control Converges [Citation Graph (0, 0)][DBLP]
    CoRR, 2003, v:0, n:, pp:- [Journal]
  8. Istvan Szita, Bálint Takács, András Lörincz
    MDPs: Learning in Varying Environments. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2002, v:3, n:, pp:145-174 [Journal]
  9. Istvan Szita, András Lörincz
    Kalman Filter Control Embedded into the Reinforcement Learning Framework. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2004, v:16, n:3, pp:491-499 [Journal]
  10. Istvan Szita, András Lörincz
    Learning Tetris Using the Noisy Cross-Entropy Method. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2006, v:18, n:12, pp:2936-2941 [Journal]
  11. Istvan Szita, András Lörincz
    Low-complexity modular policies: learning to play Pac-Man and a new framework beyond MDPs [Citation Graph (0, 0)][DBLP]
    CoRR, 2006, v:0, n:, pp:- [Journal]

  12. Monte-Carlo Tree Search in Settlers of Catan. [Citation Graph (, )][DBLP]

  13. The many faces of optimism: a unifying approach. [Citation Graph (, )][DBLP]

  14. Optimistic initialization and greediness lead to polynomial time learning in factored MDPs. [Citation Graph (, )][DBLP]

  15. Model-based reinforcement learning with nearly tight exploration complexity bounds. [Citation Graph (, )][DBLP]

  16. Monte-Carlo Tree Search: A New Framework for Game AI. [Citation Graph (, )][DBLP]

  17. Factored Value Iteration Converges. [Citation Graph (, )][DBLP]

  18. Online variants of the cross-entropy method [Citation Graph (, )][DBLP]

  19. Factored Value Iteration Converges [Citation Graph (, )][DBLP]

  20. The many faces of optimism - Extended version [Citation Graph (, )][DBLP]

  21. Optimistic Initialization and Greediness Lead to Polynomial Time Learning in Factored MDPs - Extended Version [Citation Graph (, )][DBLP]

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