There is increasing research interest in solving routing problems in sensor networks subject to constraints such as data correlation, link reliability and energy conservation. Sin...
In recent years, the evolution of artificial neural networks or neuroevolution has brought promising results in solving difficult reinforcement learning problems. But, like standa...
Today, with the advances of computer storage and technology, there are huge datasets available, offering an opportunity to extract valuable information. Probabilistic approaches ...
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
The control of overt visual attention relies on an interplay of bottom-up and top-down mechanisms. Purely bottom-up models may provide a reasonable account of the looking behaviors...