It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...
We propose cross-layer optimization frameworks for multihop wireless networks using cooperative diversity. These frameworks provide solutions to fundamental relaying problems of de...
—Most prior studies on wireless spatial-reuse TDMA (STDMA) link scheduling for throughput optimization deal with the situation where instantaneous channel state information (CSI)...
A significant body of work in multiagent systems over more than two decades has focused on multi-agent coordination (1). Many challenges in multi-agent coordination can be modeled ...
This paper is about Reinforcement Learning (RL) applied to online parameter tuning in Stochastic Local Search (SLS) methods. In particular a novel application of RL is considered i...