How can I build safe, scalable multi-agent RL apps?
Scalable Safe Multi-Agent Reinforcement Learning for Multi-Agent System
This paper introduces SS-MARL, a novel multi-agent reinforcement learning framework designed to improve the safety and scalability of multi-agent systems, particularly in real-world applications with physical constraints like robotics. It uses a constrained joint policy optimization method to ensure agents adhere to safety limitations while maximizing overall reward. Crucially for scalability, SS-MARL leverages the inherent graph structure of multi-agent environments, utilizing a multi-layer message passing network (similar to GNNs) to aggregate information, enabling zero-shot transfer learning from smaller to larger multi-agent systems. This graph-based approach also addresses partial observability, a common challenge in decentralized multi-agent setups. The performance is validated in simulated multi-agent particle environments and also with hardware implementation on Mecanum-wheeled robots. These qualities align with addressing the non-stationarity challenges prominent in LLM-based multi-agent systems, suggesting potential applicability to complex language-based agent interactions where scalability and safety guarantees are paramount.