Adaptive Algorithms in Distributed Resource Allocation

The allocation of scarce, reusable resources over time to interconnected tasks in uncertain and dynamic environments in order to optimize a performance measure is a general problem which arises in many real-world domains. The paper overviews several recent distributed approaches to this problem and compares their properties, such as the guarantees of finding a (near-) optimal solution, their robustness against different disturbances or against imprecise, uncertain models, with a special emphasis on their adaptive capabilities. The paper argues that reinforcement learning based distributed resource allocation systems represent one of the most promising approaches to these kinds of problems.