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We present an on-line crosslayer control technique to obtain policies for wireless networks. Our approach combines network utility maximization and adaptive modulation over an infinite discrete-time horizon using a class of performance measures we call ime smoothed utility functions. We model the system as an average-cost Markov decision problem. Model approximations are used to find suitable basis...
TD learning and its refinements are powerful tools for approximating the solution to dynamic programming problems. However, the techniques provide the approximate solution only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen? The goal of this paper is to propose an approach for TD learning based on choosing the...
This paper presents a modified R-learning according to the traditional average reward reinforcement learning algorithm. Reinforcement learning problems constitute an important class of learning and control problems faced by artificial intelligence systems. The general framework of reinforcement learning can be divided into two forms, discounted reward reinforcement learning and average reward reinforcement...
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