Learning predictive state representations in dynamical systems without reset

  title={Learning predictive state representations in dynamical systems without reset},
  author={Britton Wolfe and Michael R. James and Satinder P. Singh},
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
Highly Influential
This paper has highly influenced 10 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 85 citations. REVIEW CITATIONS
57 Citations
2 References
Similar Papers


Publications citing this paper.
Showing 1-10 of 57 extracted citations

85 Citations

Citations per Year
Semantic Scholar estimates that this publication has 85 citations based on the available data.

See our FAQ for additional information.


Publications referenced by this paper.
Showing 1-2 of 2 references

Similar Papers

Loading similar papers…