Tag: reinforcement learning

Competitive Influence Maximization in Trust-Based Social Networks With Deep Q-Learning (2024)

Studia Universitatis Babeș-Bolyai Informatica Authors Anikó Kopacz Abstract Social network analysis is a rapidly evolving research area having several real-life application areas, e.g. digital marketing, epidemiology, spread of misinformation. Influence maximization aims to select a subset of nodes in such manner that the information propagated over the network is maximized. Competitive influence maximization, which…

Evaluating cooperative-competitive dynamics with deep Q-learning (2023)

Neurocomputing Authors Anikó Kopacz, Lehel Csató, Camelia Chira Abstract We model cooperative-competitive social group dynamics with multi-agent environments, specialized in cases with a large number of agents from only a few distinct types. The multi-agent optimization problems are addressed in turn with multi-agent reinforcement learning algorithms to obtain flexible and robust solutions. We analyze…

Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem (2022)

International Symposium on Applied Machine Intelligence and Informatics Authors Anikó Kopacz, Ágnes Mester, S'andor Kolumb'an, Lehel Csató Abstract We propose a train rescheduling algorithm which applies a standardized feature selection based on pairwise conflicts in order to serve as input for the reinforcement learning framework. We implement an analytical method which identifies and optimally…

Applying Deep Q-learning for Multi-agent Cooperative-Competitive Environments (2022)

Soft Computing Models in Industrial and Environmental Applications Authors Anikó Kopacz, L. Csató, Camelia Chira Abstract Cooperative-competitive social group dynamics may be modelled with multi-agent environments with a large number of agents from a few distinct agent-types. Even the simplest games modelling social interactions are suitable to analyze emerging group dynamics. In many cases,…