Abstract
Pure peer-to-peer networks serve to secure information in a decentralized, distributed topology. The multi-armed bandit (MAB) problem formulation proves to be a useful tool for analyzing the problem of optimizing new peer connections. In this paper, we outline the new peer scenario described as a reinforcement learning problem with MABs in order to identify the fastest peer to download from during the connection process. The MAB problem involves k slot machines which are also called one-armed bandits and pay out reward values according to an internal distribution, of which the agent is not aware. The aim is to choose a strategy to learn which arms pay out the most in order to maximize total reward over a set number of rounds. Results indicate that UCB and \(\varepsilon \)-first performed the best at selecting the optimal peer in each of our test scenarios. Contrariwise, SoftMax and \(\varepsilon \)-greedy unperformed.
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Karras, A., Karras, C., Giotopoulos, K.C., Giannoukou, I., Tsolis, D., Sioutas, S. (2022). Download Speed Optimization in P2P Networks Using Decision Making and Adaptive Learning. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the ICR’22 International Conference on Innovations in Computing Research. ICR 2022. Advances in Intelligent Systems and Computing, vol 1431. Springer, Cham. https://doi.org/10.1007/978-3-031-14054-9_22
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