509 search results - page 19 / 102 » Using Learning for Approximation in Stochastic Processes |
NIPS
1993
13 years 8 months ago
1993
Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms,includ...
ICML
2006
IEEE
14 years 8 months ago
2006
IEEE
Policy evaluation is a critical step in the approximate solution of large Markov decision processes (MDPs), typically requiring O(|S|3 ) to directly solve the Bellman system of |S...
KR
1992
Springer
13 years 11 months ago
1992
Springer
While the task of answering queries from an arbitrary propositional theory is intractable in general, it can typicallybe performed e ciently if the theory is Horn. This suggests t...
WSC
2007
13 years 9 months ago
2007
We consider the problem of pricing American options when the volatility of the underlying asset price is stochastic. No specific stochastic volatility model is assumed for the st...
CORR
2012
Springer
12 years 3 months ago
2012
Springer
Abstract— In this paper, we consider a class of continuoustime, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation ...