CAMA: A New Framework for Safe Multi-Agent Reinforcement Learning Using Constraint Augmentation

Ziyan Wang, Yali Du, Aivar Sootla, Haitham Bou Ammar, Jun Wang

Keywords: Safe, Multi-agent Reinforcement Learning, Augmentation

TL;DR: CAMA can combine any SOTA non-safe MARL algorithms to ensure they satisfied added constraints without strong assumptions and complex implementations.

Abstract: With the widespread application of multi-agent reinforcement learning (MARL) in real-life settings, the ability to meet safety constraints has become an urgent problem to solve. For example, it is necessary to avoid collisions to reach a common goal in controlling multiple drones. We address this problem by introducing the Constraint Augmented Multi-Agent framework --- CAMA. CAMA can serve as a plug-and-play module to the popular MARL algorithms, including centralized training, decentralized execution and independent learning frameworks. In our approach, we represent the safety constraint as the sum of discounted safety costs bounded by the predefined value, which we call the safety budget. Experiments demonstrate that CAMA can converge quickly to a high degree of constraint satisfaction and surpasses other state-of-the-art safety counterpart algorithms in both cooperative and competitive settings.

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