Challenges and opportunities in deep reinforcement learning with graph neural networks: A comprehensive review of algorithms and applications
Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …
Reinforcement learning for generative ai: A survey
Deep Generative AI has been a long-standing essential topic in the machine learning
community, which can impact a number of application areas like text generation and …
community, which can impact a number of application areas like text generation and …
Graph neural networks for intelligent transportation systems: A survey
Graph neural networks (GNNs) have been extensively used in a wide variety of domains in
recent years. Owing to their power in analyzing graph-structured data, they have become …
recent years. Owing to their power in analyzing graph-structured data, they have become …
[HTML][HTML] A graph reinforcement learning framework for neural adaptive large neighbourhood search
Abstract Adaptive Large Neighbourhood Search (ALNS) is a popular metaheuristic with
renowned efficiency in solving combinatorial optimisation problems. However, despite 18 …
renowned efficiency in solving combinatorial optimisation problems. However, despite 18 …
Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective
VA Darvariu, S Hailes, M Musolesi - arXiv preprint arXiv:2404.06492, 2024 - arxiv.org
Graphs are a natural representation for systems based on relations between connected
entities. Combinatorial optimization problems, which arise when considering an objective …
entities. Combinatorial optimization problems, which arise when considering an objective …
Intrinsically motivated graph exploration using network theories of human curiosity
Intrinsically motivated exploration has proven useful for reinforcement learning, even without
additional extrinsic rewards. When the environment is naturally represented as a graph, how …
additional extrinsic rewards. When the environment is naturally represented as a graph, how …
SCGG: A deep structure-conditioned graph generative model
Deep learning-based graph generation approaches have remarkable capacities for graph
data modeling, allowing them to solve a wide range of real-world problems. Making these …
data modeling, allowing them to solve a wide range of real-world problems. Making these …
Deep Graph Reinforcement Learning for Solving Multicut Problem
Z Li, X Yang, Y Zhang, S Zeng, J Yuan… - … on Neural Networks …, 2024 - ieeexplore.ieee.org
The multicut problem, also known as correlation clustering, is a classic combinatorial
optimization problem that aims to optimize graph partitioning given only node (dis) …
optimization problem that aims to optimize graph partitioning given only node (dis) …
Planning spatial networks with Monte Carlo tree search
VA Darvariu, S Hailes… - Proceedings of the …, 2023 - royalsocietypublishing.org
We tackle the problem of goal-directed graph construction: given a starting graph, finding a
set of edges whose addition maximally improves a global objective function. This problem …
set of edges whose addition maximally improves a global objective function. This problem …
Dynamic network reconfiguration for entropy maximization using deep reinforcement learning
C Doorman, VA Darvariu, S Hailes… - Learning on Graphs …, 2022 - proceedings.mlr.press
A key problem in network theory is how to reconfigure a graph in order to optimize a
quantifiable objective. Given the ubiquity of networked systems, such work has broad …
quantifiable objective. Given the ubiquity of networked systems, such work has broad …