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  1. @book{Bar-Shalom:1987,
  2. author = {Bar-Shalom, Y.},
  3. title = {Tracking and Data Association},
  4. year = {1987},
  5. isbn = {0-120-79760-7},
  6. publisher = {Academic Press Professional, Inc.},
  7. address = {San Diego, CA, USA},
  8. }
  9. @ARTICLE{Cox93areview,
  10. author = {Ingemar J. Cox},
  11. title = {A Review of Statistical Data Association Techniques for Motion Correspondence},
  12. journal = {International Journal of Computer Vision},
  13. year = {1993},
  14. volume = {10},
  15. pages = {53--66}
  16. }
  17. @inproceedings{hans_efficient_2009,
  18. author = {Hans, Alexander and Udluft, Steffen},
  19. booktitle = {International {{Conference}} on {{Artificial Neural Networks}}},
  20. date = {2009},
  21. pages = {70--79},
  22. publisher = {{Springer}},
  23. title = {Efficient Uncertainty Propagation for Reinforcement Learning with Limited Data}
  24. }
  25. @article{tresp_wet_1994,
  26. author = {Tresp, Volker},
  27. date = {1994},
  28. journaltitle = {Siemens AG, CT IC 4, Technical Report},
  29. title = {The Wet Game of Chicken}
  30. }
  31. @article{kaiser_interpretable_2019,
  32. abstract = {In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learning scheme based on variational inference. This scheme is applied to a heteroskedastic and bimodal benchmark problem on which we compare our results to NFQ and show how our approach yields human-interpretable insight about the underlying dynamics while also increasing data-efficiency.},
  33. author = {Kaiser, Markus and Otte, Clemens and Runkler, Thomas and Ek, Carl Henrik},
  34. date = {2019},
  35. journaltitle = {Computational Intelligence and Machine Learning},
  36. langid = {english},
  37. pages = {6},
  38. title = {Interpretable {{Dynamics Models}} for {{Data}}-{{Efficient Reinforcement Learning}}},
  39. volume = {ESANN 2019 proceedings}
  40. }