Robust Policy Learning for Multi-UAV Collision Avoidance with Causal Feature Selection

TL;DR

This paper introduces a causal feature selection module for deep reinforcement learning-based UAV collision avoidance, significantly improving generalization and robustness in unseen environments.

Contribution

It proposes a novel causal feature selection method that enhances policy robustness by filtering out non-causal factors in UAV navigation.

Findings

Outperforms existing algorithms in unseen environments

Improves collision avoidance robustness

Reduces influence of spurious correlations

Abstract

In unseen and complex outdoor environments, collision avoidance navigation for unmanned aerial vehicle (UAV) swarms presents a challenging problem. It requires UAVs to navigate through various obstacles and complex backgrounds. Existing collision avoidance navigation methods based on deep reinforcement learning show promising performance but suffer from poor generalization abilities, resulting in performance degradation in unseen environments. To address this issue, we investigate the cause of weak generalization ability in DRL and propose a novel causal feature selection module. This module can be integrated into the policy network and effectively filters out non-causal factors in representations, thereby reducing the influence of spurious correlations between non-causal factors and action predictions. Experimental results demonstrate that our proposed method can achieve robust navigation performance and effective collision avoidance especially in scenarios with unseen backgrounds and obstacles, which significantly outperforms existing state-of-the-art algorithms.