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...
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...
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 ...
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
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...