Humans can learn under a wide variety of feedback conditions. Particularly important types of learning fall under the category of reinforcement learning (RL) where a series of decisions must be made and a sparse feedback signal is obtained. Computational and behavioral studies of RL have focused mainly on Markovian decision processes (MDPs), where the next state and reward depends only on the c...

The output-feedback control problem is solved for continuous-time, linear, retarded and neutral type systems. A delay-dependent solution is obtained in terms of linear matrix inequalities (LMIs) by using a descriptor model transformation of the system and by applying Park’s inequality for bounding cross terms. A state-feedback solution is derived for systems with polytopic parameter uncertainti...

We perform feedback experiments and simulations in which a colloidal dumbbell particle, acting as particle on ring, is followed by repulsive optical trap controlled continuous-time-delayed protocol. The dynamics are described persistent random walk similarly to that of an active Brownian with transition from predominantly diffusive driven behavior at critical delay time. model the short long re...

Conversion rate (CVR) prediction is one of the most critical tasks for digital display advertising. Commercial systems often require to update models in an online learning manner catch up with evolving data distribution. However, conversions usually do not happen immediately after user clicks. This may result inaccurate labeling, which called delayed feedback problem. In previous studies, probl...