575 search results - page 69 / 115 » Reinforcement Learning State Estimator |
ICML
2010
IEEE
14 years 11 months ago
2010
IEEE
This paper formalizes Feature Selection as a Reinforcement Learning problem, leading to a provably optimal though intractable selection policy. As a second contribution, this pape...
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PKDD
2010
Springer
14 years 7 months ago
2010
Springer
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
NIPS
2008
14 years 11 months ago
2008
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of...
ICML
2001
IEEE
15 years 10 months ago
2001
IEEE
Direct policy search is a practical way to solve reinforcement learning problems involving continuous state and action spaces. The goal becomes finding policy parameters that maxi...
ICCS
1993
Springer
15 years 2 months ago
1993
Springer
Adaptive predictive search (APS), is a learning system framework, which given little initial domain knowledge, increases its decision-making abilities in complex problems domains....