This work focuses on the convergence analysis of adaptive distributed beamforming schemes that can be reformulated as local random search algorithms via a random search framework....
Abstract. In this paper we compare state-of-the-art multi-agent reinforcement learning algorithms in a wide variety of games. We consider two types of algorithms: value iteration a...
H. Jaap van den Herik, Daniel Hennes, Michael Kais...
We consider a multi-agent optimization problem where agents aim to cooperatively minimize a sum of local objective functions subject to a global inequality constraint and a global ...
Existing autocalibration techniques use numerical optimization algorithms that are prone to the problem of local minima. To address this problem, we have developed a method where ...
This paper deals with clustering of spatially distributed data using wireless sensor networks. A distributed low-complexity clustering algorithm is developed that requires one-hop...
Pedro A. Forero, Alfonso Cano, Georgios B. Giannak...