In this paper, we adopt general-sum stochastic games as a framework for multiagent reinforcement learning. Our work extends previous work by Littman on zero-sum stochastic games t...
Abstract— The most widely applied resource allocation strategy is to balance, or equalize, the total workload assigned to each resource. In mobile multi-agent systems, this princ...
Marco Pavone, Alessandro Arsie, Emilio Frazzoli, F...
In this paper, we propose a set of dimensioning rules, which deliver high quality sessionbased services over a Next Generation Network based IP/MPLS transport infrastructure. In p...
— We consider the issue of fair share of the spectrum opportunity for the case of spectrum-overlay cognitive radio networks. Owing to the decentralized nature of the network, we ...
Abstract— We present a strategy for resolving multiple hypotheses of a robot’s state during global localization. The strategy operates in two stages. In the first stage a uniq...
Rakesh Goyal, K. Madhava Krishna, Shivudu Bhuvanag...