Model-Based Graph Reinforcement Learning for Inductive Traffic Signal Control
We introduce MuJAM, an adaptive traffic signal control method which leverages model-based reinforcement learning to 1) extend recent generalization efforts (to road network architectures and traffic distributions) further by allowing a generalization to the controllers’ constraints (cycli...
Saved in:
Main Authors: | Francois-Xavier Devailly, Denis Larocque, Laurent Charlin |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Open Journal of Intelligent Transportation Systems |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10470423/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control
by: Tianyu Shi, et al.
Published: (2024-01-01) -
Multi-Agent Hierarchical Graph Attention Actor–Critic Reinforcement Learning
by: Tongyue Li, et al.
Published: (2024-12-01) -
IALight: Importance-Aware Multi-Agent Reinforcement Learning for Arterial Traffic Cooperative Control
by: Lu WEI, et al.
Published: (2025-02-01) -
Reinforcement Learning-Based Autonomous Soccer Agents: A Study in Multi-Agent Coordination and Strategy Development
by: Biplov Paneru, et al.
Published: (2025-01-01) -
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research
by: Jan Benad, et al.
Published: (2025-02-01)