Graph representations for reinforcement learning
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Date
2024-04
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Universidad Nacional de La Plata. Facultad de Informática.
Abstract
Graph analysis is becoming increasingly important due to the expressive power of graph models and the
efficient algorithms available for processing them. Reinforcement Learning is one domain that could ben-
efit from advancements in graph analysis, given that a learning agent may be integrated into an environ-
ment that can be represented as a graph. Nevertheless, the structural irregularity of graphs and the lack
of prior labels make it difficult to integrate such a model into modern Reinforcement Learning frameworks that rely on artificial neural networks. Graph embedding enables the learning of low-dimensional vector representations that are more suited for machine learning algorithms, while retaining essential graph features. This paper presents a framework for evaluating graph embedding algorithms and their ability to preserve the structure and relevant features of graphs by means of an internal validation metric, without resorting to subsequent tasks that require labels for training. Based on this framework, three defined algorithms that meet the necessary requirements for solving a specific problem of Reinforcement Learningin graphs are selected, analyzed, and compared. These algorithms are Graph2Vec, GL2Vec, and Wavelet Characteristics, with the latter two demonstrating superior performance.
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Keywords
Computational intelligence, Reinforce- ment learning, Graph embeddings, Unsupervised GRL, Whole graph embedding
Citation
Journal of Computer Science and Technology
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Creative Commons license
Except where otherwised noted, this item's license is described as info:eu-repo/semantics/openAccess