Paper “Distributed Consensus-Based Multi-Agent Temporal-Difference Learning,” by Milos S. Stankovic, Marko Beko and Srdjan S. Stankovic , has been presented at the 60th IEEE Conference on Decision and Control (CDC), December 2021.
Paper “Application of Deep Learning Algorithms and Architectures in the New Generation of Mobile Networks“, by Dejan Dašić, Miljan Vučetić, Nemanja Ilić, Miloš Stanković and Marko Beko, has been published in Serbian Journal of Electrical Engineering.
The paper presents a detailed overview of state-of-the-art applications and services related to the new generation of mobile networks that employ deep learning methods. Modern architectures used for their deployment have also been discussed. The paper also presents a practical use case of modulation classification using deep learning in an application essential for modern spectrum management.
Paper “Distributed Value Function Approximation for Collaborative Multi-Agent Reinforcement Learning“, by M.S. Stanković, M. Beko and S.S. Stanković, has been published in IEEE Transactions on Control of Network Systems (IEEE TCNS)!
In the paper, several new distributed gradient-based temporal difference algorithms for decentralized multi-agent off-policy learning of the value function in Markov decision processes were proposed, rigorously theoretically analyzed and verified using extensive simulations.
Paper “Consensus on the Auxiliary Variables in Distributed Gradient-Based Temporal Difference Algorithms,” by M.S. Stankovic, N. Ilic, M. Beko, and S.S. Stankovic, has been presented at the IcETRAN Conference , September 2021.
Chapter “Feedforward Multi-Layer Perceptron Training by Hybridized Method between Genetic Algorithm and Artificial Bee Colony” by Aleksa Cuk, Timea Bezdan, Nebojsa Bacanin, Miodrag Zivkovic, K. Venkatachalam, Tarik A. Rashid, and V. Kanchana Devi, has been published in book “Data Science and Data Analytics – Opportunities and Challenges“, edited by Amit Kumar Tyagi.
Paper “Distributed Consensus-Based Multi-Agent Temporal-Difference Learning,” by Milos S. Stankovic, Marko Beko and Srdjan S. Stankovic, has been accepted for presentation at the renowned IEEE Conference on Decision and Control (CDC) in December 2021.
Paper “Advanced Hierarchical Predictive Routing Control of a Smart De-Manufacturing Plant,” by R. Boffadossi, L. Fagiano, A. Cataldo, M. Tanaskovic, and M. Lauricella, has been presented at the European Control Conference (ECC) , July 2021.
Paper “Distributed Spectrum Management in Cognitive Radio Networks by Consensus-Based Reinforcement Learning“, by D. Dašić, N. Ilić, M. Vučetić, M. Perić, M. Beko and M.S. Stanković, has been published in journal Sensors!
In the paper, the authors proposed a new algorithm for distributed spectrum management in Cognitive Radio Networks (CRN) based on a multi-agent reinforcement learning scheme. The paper presents a detailed discussion and analysis of the algorithm’s properties, together with extensive simulations illustrating the effectiveness and advantages of the proposed scheme.
Paper “Obuka perceptrona hibridizovanom metodom između genetskog algoritma i Firefly algoritma”, by A. Ćuk, U. Dragović, M. Tanasković, N. Bačanin and M.S. Stanković, has been presented at conference YUINFO 2021, March 2021.
International monograph “Decentralized Consensus-Based Estimation and Target Tracking“ by M.S. Stanković, N. Ilić and S.S. Stanković has been published by Academic Mind, Belgrade (ISBN 978-86-7466-859-7).