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.
Causal Multi-Agent Reinforcement Learning: Review and Open Problems
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abstract
This paper serves to introduce the reader to the field of multi-agent reinforcement learning (MARL) and its intersection with methods from the study of causality. We highlight key challenges in MARL and discuss these in the context of how causal methods may assist in tackling them. We promote moving toward a 'causality first' perspective on MARL. Specifically, we argue that causality can offer improved safety, interpretability, and robustness, while also providing strong theoretical guarantees for emergent behaviour. We discuss potential solutions for common challenges, and use this context to motivate future research directions.
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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.