%0 Journal Article %1 szepesvari2001 %A Szepesvári, Cs. %D 2001 %J AI Communications %K Lipschitz-continuous MDPs, Monte-Carlo analysis bounds, complexity continuous-space curse dimensionality, methods, of performance theory, %N 3 %P 163--176 %T Efficient Approximate Planning in Continuous Space Markovian Decision Problems %V 13 %X Monte-Carlo planning algorithms for planning in continuous state-space, discounted Markovian Decision Problems (MDPs) having a smooth transition law and a finite action space are considered. We prove various polynomial complexity results for the considered algorithms, improving upon several known bounds.

@article{szepesvari2001, abstract = {Monte-Carlo planning algorithms for planning in continuous state-space, discounted Markovian Decision Problems (MDPs) having a smooth transition law and a finite action space are considered. We prove various polynomial complexity results for the considered algorithms, improving upon several known bounds.}, added-at = {2020-03-17T03:03:01.000+0100}, author = {Szepesv{\'a}ri, {Cs}.}, biburl = {https://www.bibsonomy.org/bibtex/23e1bdac4956598d0a37c8cea1278515a/csaba}, date-added = {2010-08-28 17:38:14 -0600}, date-modified = {2010-09-02 13:09:16 -0600}, interhash = {1e8b36afede66e400c2e5b07a50d95ed}, intrahash = {3e1bdac4956598d0a37c8cea1278515a}, journal = {{AI} Communications}, keywords = {Lipschitz-continuous MDPs, Monte-Carlo analysis bounds, complexity continuous-space curse dimensionality, methods, of performance theory,}, number = 3, pages = {163--176}, pdf = {papers/aicom.pdf}, timestamp = {2020-03-17T03:03:01.000+0100}, title = {Efficient Approximate Planning in Continuous Space {M}arkovian Decision Problems}, volume = 13, year = 2001 }