Tackling Safe and Efficient Multi-Agent Reinforcement Learning via Dynamic Shielding (Student Abstract)

Multi-agent Reinforcement Learning (MARL) has been increasingly used in safety-critical applications but has no safety guarantees, especially during training. In this paper, we propose dynamic shielding, a novel decentralized MARL framework to ensure safety in both training and deployment phases. Our framework leverages Shield, a reactive system running in parallel with the reinforcement learning algorithm to monitor and correct agents' behavior. In our algorithm, shields dynamically split and merge according to the environment state in order to maintain decentralization and avoid conservative behaviors while enjoying formal safety guarantees. We demonstrate the effectiveness of MARL with dynamic shielding in the mobile navigation scenario.

Authors

• Wenli Xiao The Chinese University of Hong Kong, Shenzhen
• Yiwei Lyu University of Michigan - Ann Arbor
• John M. Dolan Carnegie Mellon University

Keywords

• Multiagent Systems
• Reinforcement Learning
• robotics
• Safe Reinforcement Learning