We target the problem of closed-loop learning of control policies that map visual percepts to continuous actions. Our algorithm, called Reinforcement Learning of Joint Classes (RLJ...
—A major threat to data networks is based on the fact that some traffic can be expensive to classify and filter as it will undergo a longer than average list of filtering rule...
Planning in partially-observable dynamical systems is a challenging problem, and recent developments in point-based techniques such as Perseus significantly improve performance as...
We seek to increase user confidence in simulations as they are adapted to meet new requirements. Our approach includes formal representation of uncertainty, lightweight validation,...
Paul F. Reynolds Jr., Michael Spiegel, Xinyu Liu, ...
This paper presents a modified optimal control model of drug scheduling in cancer chemotherapy and a new adaptive elitist-population based genetic algorithm (AEGA) to solve it. Wo...