PEnGUiN让图神经网络在不对称环境中更高效，提升多智能体学习的鲁棒性。
PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL
- 提出部分等变图网络，统一处理对称与非对称场景
- 在含多种不对称性的任务中，性能超越EGNN和标准GNN
- 适合需要高样本效率的真实复杂多智能体系统
等变图神经网络（EGNN）在多智能体强化学习（MARL）中展现出巨大潜力，凭借对称性保证显著提升样本效率与泛化能力。然而，现实环境常因外力、测量误差或系统固有偏差产生内在不对称性。本文提出部分等变图神经网络（PEnGUiN），专门应对此类挑战。我们形式化识别并分类了多种与MARL相关的部分等变性，包括子群等变、特征级等变、区域等变及近似等变。理论证明PEnGUiN可在统一框架内学习完全等变（EGNN）与非等变（GNN）表示。在包含多种不对称性的多个MARL任务上进行广泛实验，结果表明PEnGUiN在异构环境中持续优于EGNN与标准GNN，凸显其在真实场景中提升基于图的MARL算法鲁棒性与适用性的潜力。
原文摘要 · Abstract (English)
Equivariant Graph Neural Networks (EGNNs) have emerged as a promising approach in Multi-Agent Reinforcement Learning (MARL), leveraging symmetry guarantees to greatly improve sample efficiency and generalization. However, real-world environments often exhibit inherent asymmetries arising from factors such as external forces, measurement inaccuracies, or intrinsic system biases. This paper introduces \textit{Partially Equivariant Graph NeUral Networks (PEnGUiN)}, a novel architecture specifically designed to address these challenges. We formally identify and categorize various types of partial equivariance relevant to MARL, including subgroup equivariance, feature-wise equivariance, regional equivariance, and approximate equivariance. We theoretically demonstrate that PEnGUiN is capable of learning both fully equivariant (EGNN) and non-equivariant (GNN) representations within a unified framework. Through extensive experiments on a range of MARL problems incorporating various asymmetries, we empirically validate the efficacy of PEnGUiN. Our results consistently demonstrate that PEnGUiN outperforms both EGNNs and standard GNNs in asymmetric environments, highlighting their potential to improve the robustness and applicability of graph-based MARL algorithms in real-world scenarios.
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