Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
We address performance issues associated with simulationbased algorithms for optimizing Markov reward processes. Specifically, we are concerned with algorithms that exploit the re...
Small-world networks have become an important model for understanding many complex phenomena in science and in sociological contexts. One tool for exploring the critical and phase...
Abstract--It was shown recently that carrier sense multiple access (CSMA)-like distributed algorithms can achieve the maximal throughput in wireless networks (and task processing n...
This paper considers the least-square online gradient descent algorithm in a reproducing kernel Hilbert space (RKHS) without explicit regularization. We present a novel capacity i...