This paper describes a computationally feasible approximation to the AIXI agent, a universal reinforcement learning agent for arbitrary environments. AIXI is scaled down in two ke...
Joel Veness, Kee Siong Ng, Marcus Hutter, William ...
An approximate rank revealing factorization problem with structure constraints on the normalized factors is considered. Examples of structure, motivated by an application in micro...
Abstract— We consider a scenario in which frequency agile radios opportunistically share a fixed spectrum resource with a set of primary nodes. We develop a collaborative scheme...
-We solve the problem of time-optimal network queue control: what are the input data rates that make network queue sizes converge to their ideal size in the least possible time aft...
Cognitive radio networks are emerging as a promising technology for the efficient use of radio spectrum. In these networks, there are two categories of networks on different chann...