This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
Joel Veness, Kee Siong Ng, Marcus Hutter, David Si...
We develop a closed-form approximation algorithm for designing IIR digital filters with linear phase and guaranteed stability. In this algorithm, the stopband and passband edge f...
We develop a framework for obtaining Fully Polynomial Time Approximation Schemes (FPTASs) for stochastic univariate dynamic programs with either convex or monotone single-period c...
Nir Halman, Diego Klabjan, Chung-Lun Li, James B. ...
We study approximation solutions for the densest k-subgraph problem (DS-k) on several classes of intersection graphs. We adopt the concept of -quasi elimination orders, introduced ...
During the last years, speed-up techniques for DIJKSTRA’s algorithm have been developed that make the computation of shortest paths a matter of microseconds even on huge road net...
Reinhard Bauer, Gianlorenzo D'Angelo, Daniel Delli...