Heterogeneous systems, systems with multiple processors tailored for specialized tasks, are challenging programming environments. While it may be possible for domain experts to op...
Andrew Kerr, Gregory F. Diamos, Sudhakar Yalamanch...
In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multi-agent tasks. We introduce a hierarchical multi-a...
Rajbala Makar, Sridhar Mahadevan, Mohammad Ghavamz...
We present a combinatorial framework for the study of a natural class of distributed optimization problems that involve decisionmaking by a collection of n distributed agents in th...
In this paper we study the topic of CBR systems learning from observations in which those observations can be represented as stochastic policies. We describe a general framework wh...
Kellen Gillespie, Justin Karneeb, Stephen Lee-Urba...
Adaptive mesh refinement (AMR) is a powerful technique that reduces the resources necessary to solve otherwise intractable problems in computational science. The AMR strategy solv...
Michael L. Welcome, Charles A. Rendleman, Leonid O...