509 search results - page 42 / 102 » Using Learning for Approximation in Stochastic Processes |
134
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AIPS
2008
15 years 4 months ago
2008
Enforced hill-climbing is an effective deterministic hillclimbing technique that deals with local optima using breadth-first search (a process called "basin flooding"). ...
138
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JMLR
2010
14 years 9 months ago
2010
Real stochastic processes operating in continuous time can be modeled by sets of stochastic differential equations. On the other hand, several popular model families, including hi...
115
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ENTCS
2010
15 years 2 months ago
2010
Process modeling languages such as "Dynamical Grammars" are highly expressive in the processes they model using stochastic and deterministic dynamical systems, and can b...
116
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LICS
2007
IEEE
15 years 8 months ago
2007
IEEE
Markov decision processes (MDPs) are controllable discrete event systems with stochastic transitions. The payoff received by the controller can be evaluated in different ways, dep...
132
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ROBOCUP
2007
Springer
15 years 8 months ago
2007
Springer
Reinforcement learning is a paradigm under which an agent seeks to improve its policy by making learning updates based on the experiences it gathers through interaction with the en...