Searching the space of policies directly for the optimal policy has been one popular method for solving partially observable reinforcement learning problems. Typically, with each ...
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
In this paper we describe a novel approach to the visualization of the mapping between two schemas. Current approaches to visually defining such a mapping fail when the schemas or...
George G. Robertson, Mary Czerwinski, John E. Chur...
In Computer Vision, two-dimensional shape classifcation is a complex and well studied topic, often basic for three-dimensional object recognition. Object contours are a widely cho...
In this paper, we study the interdependency between leakage energy and chip temperature in real-time systems. We observe that the temperature variation on chip has a large impact ...