Causal-Inspired Multi-Agent Decision-Making via Graph Reinforcement Learning

TL;DR

This paper introduces a novel approach combining causal learning and graph reinforcement learning to improve multi-agent decision-making in autonomous driving, leading to safer and more efficient navigation in complex traffic scenarios.

Contribution

It proposes integrating causal disentanglement with graph reinforcement learning to enhance decision-making in autonomous vehicles at intersections.

Findings

Achieves highest average reward during training.

Significantly reduces collision rates.

Outperforms existing methods in key metrics.

Abstract

Since the advent of autonomous driving technology, it has experienced remarkable progress over the last decade. However, most existing research still struggles to address the challenges posed by environments where multiple vehicles have to interact seamlessly. This study aims to integrate causal learning with reinforcement learning-based methods by leveraging causal disentanglement representation learning (CDRL) to identify and extract causal features that influence optimal decision-making in autonomous vehicles. These features are then incorporated into graph neural network-based reinforcement learning algorithms to enhance decision-making in complex traffic scenarios. By using causal features as inputs, the proposed approach enables the optimization of vehicle behavior at an unsignalized intersection. Experimental results demonstrate that our proposed method achieves the highest average reward during training and our approach significantly outperforms other learning-based methods in several key metrics such as collision rate and average cumulative reward during testing. This study provides a promising direction for advancing multi-agent autonomous driving systems and make autonomous vehicles' navigation safer and more efficient in complex traffic environments.