—We consider a widely applicable model of resource allocation where two sequences of events are coupled: on a continuous time axis (t), network dynamics evolve over time. On a di...
Alexandre Proutiere, Yung Yi, Tian Lan, Mung Chian...
Two integrator backstepping designs are presented for digitally controlled continuous-time plants in special form. The controller designs are based on the Euler approximate discre...
Inference is a key component in learning probabilistic models from partially observable data. When learning temporal models, each of the many inference phases requires a complete ...
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 ...
A large class of q-distributions is defined on the stochastic model of Bernoulli trials in which the probability of success (=advancing to the next level) depends geometrically on...