graph-reinforcement-learning

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neunms / Reinforcement-learning-on-graphs-A-survey

This collection of papers can be used to summarize research about graph reinforcement learning for the convenience of researchers.

reinforcement-learning deep-learning deep-reinforcement-learning graph-neural-networks graph-reinforcement-learning

Updated Nov 29, 2024

He-JiYe / CentriLearn

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CentriLearn is a reinforcement learning framework based on graph neural networks for solving combinatorial optimization problems in complex networks, such as network dismantling.

reinforcement-learning graph-neural-networks network-dismantling graph-reinforcement-learning

Updated May 25, 2026
Python

Arash-ra03 / gnn-marl-traffic-signal-control

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GraphSAGE-enhanced SEMI-CTDE multi-agent deep reinforcement learning for adaptive traffic signal control in SUMO.

reinforcement-learning deep-reinforcement-learning pytorch sumo multi-agent-systems intelligent-transportation-systems graphsage graph-neural-networks multi-agent-reinforcement-learning double-deep-q-network traffic-signal-control dgl graph-reinforcement-learning sumo-traffic-simulator adaptive-traffic-signal-control semi-ctde

Updated May 12, 2026
Jupyter Notebook

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