Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System

• URL: http://arxiv.org/abs/2501.13727v1
• Date: Thu, 23 Jan 2025 15:01:19 GMT
• Title: Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System
• Authors: Haikuo Du, Fandi Gou, Yunze Cai,
• Abstract summary: Existing Multi-Agent Reinforcement Learning (MARL) algorithms that rely solely on reward shaping are ineffective in ensuring safety. We propose a novel framework, Scalable Safe MARL (SS-MARL), to enhance the safety and scalability of MARL methods. We show that SS-MARL achieves a better trade-off between optimality and safety compared to baselines, and its scalability significantly outperforms the latest methods in scenarios with a large number of agents.
• Score: 1.0124625066746598
• License:
• Abstract: Safety and scalability are two critical challenges faced by practical Multi-Agent Systems (MAS). However, existing Multi-Agent Reinforcement Learning (MARL) algorithms that rely solely on reward shaping are ineffective in ensuring safety, and their scalability is rather limited due to the fixed-size network output. To address these issues, we propose a novel framework, Scalable Safe MARL (SS-MARL), to enhance the safety and scalability of MARL methods. Leveraging the inherent graph structure of MAS, we design a multi-layer message passing network to aggregate local observations and communications of varying sizes. Furthermore, we develop a constrained joint policy optimization method in the setting of local observation to improve safety. Simulation experiments demonstrate that SS-MARL achieves a better trade-off between optimality and safety compared to baselines, and its scalability significantly outperforms the latest methods in scenarios with a large number of agents. The feasibility of our method is also verified by hardware implementation with Mecanum-wheeled vehicles.

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