IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

@article{Devailly2020IGRLIG,
  title={IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control},
  author={Franois-Xavier Devailly and Denis Larocque and Laurent Charlin},
  journal={ArXiv},
  year={2020},
  volume={abs/2003.05738}
}
Scaling adaptive traffic-signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning attempts to address this challenge by distributing control to specialized agents. However, specialization hinders generalization and transferability, and the computational graphs underlying neural-networks architectures---dominating in the multi-agent setting---do not offer the flexibility to handle an arbitrary number of entities which changes both between… Expand

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