Learning predictive state representations in dynamical systems without reset

@inproceedings{Wolfe2005LearningPS,
  title={Learning predictive state representations in dynamical systems without reset},
  author={Britton Wolfe and Michael R. James and Satinder P. Singh},
  booktitle={ICML},
  year={2005}
}
Predictive state representations (PSRs) are a recently-developed way to model discrete-time, controlled dynamical systems. We present and describe two algorithms for learning a PSR model: a Monte Carlo algorithm and a temporal difference (TD) algorithm. Both of these algorithms can learn models for systems without requiring a reset action as was needed by the previously available general PSR-model learning algorithm. We present empirical results that compare our two algorithms and also compare… CONTINUE READING
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