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» Randomness, Stochasticity and Approximations
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ATAL
2007
Springer
14 years 3 months ago
Model-based function approximation in reinforcement learning
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Nicholas K. Jong, Peter Stone
BMCBI
2006
127views more  BMCBI 2006»
13 years 9 months ago
On the attenuation and amplification of molecular noise in genetic regulatory networks
Background: Noise has many important roles in cellular genetic regulatory functions at the nanomolar scale. At present, no good theory exists for identifying all possible mechanis...
Bor-Sen Chen, Yu-Chao Wang
ICML
2010
IEEE
13 years 10 months ago
Toward Off-Policy Learning Control with Function Approximation
We present the first temporal-difference learning algorithm for off-policy control with unrestricted linear function approximation whose per-time-step complexity is linear in the ...
Hamid Reza Maei, Csaba Szepesvári, Shalabh ...
BMCBI
2007
172views more  BMCBI 2007»
13 years 9 months ago
msBayes: Pipeline for testing comparative phylogeographic histories using hierarchical approximate Bayesian computation
Background: Although testing for simultaneous divergence (vicariance) across different population-pairs that span the same barrier to gene flow is of central importance to evoluti...
Michael J. Hickerson, Eli Stahl, Naoki Takebayashi
WSC
2001
13 years 11 months ago
Deterministic fluid models of congestion control in high-speed networks
Congestion control algorithms, such as TCP or the closelyrelated additive increase-multiplicative decrease algorithms, are extremely difficult to simulate on a large scale. The re...
Sanjay Shakkottai, R. Srikant