A graph-RL driving agent using VGAE-based causal feature extraction achieves lower collision rates and higher rewards at a simulated unsignalized intersection than graph-RL baselines.
Graph neural networks and reinforcement learning: A survey,
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Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning
A graph-RL driving agent using VGAE-based causal feature extraction achieves lower collision rates and higher rewards at a simulated unsignalized intersection than graph-RL baselines.