Temporal Graph Traversals Using Reinforcement Learning With Proximal Policy Optimization

Temporal Graph Traversals Using Reinforcement Learning With Proximal Policy Optimization

Graphs in real-world applications are dynamic both in terms of structures and inputs. Information discovery in such networks, which present dense and deeply connected patterns locally and sparsity globally can be time consuming and computationally costly. In this paper we address the shortest path q...

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Bibliographic Details
Main Authors:	Samuel Henrique Silva, Adel Alaeddini, Peyman Najafirad
Format:	Article
Language:	English
Published:	IEEE 2020-01-01
Series:	IEEE Access
Subjects:	Machine learning graphs Markov-chain deep reinforcement learning path-planning
Online Access:	https://ieeexplore.ieee.org/document/9055369/

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