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» Randomness, Stochasticity and Approximations
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ATAL
2007
Springer
15 years 10 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»
15 years 4 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
15 years 5 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»
15 years 4 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
122
Voted
WSC
2001
15 years 5 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