Shield Decentralization for Safe Reinforcement Learning in General Partially Observable Multi-Agent Environments

Daniel Melcer, Christopher Amato, Stavros Tripakis

Abstract: As reinforcement learning (RL) is increasingly used in safety-critical systems, it is important to restrict RL agents to only take safe actions. Shielding is a promising approach to this task; however, in multi-agent domains, shielding has previously been restricted to environments where all agents observe the same information. Most real-world tasks do not satisfy this strong assumption. We discuss the theoretical foundations of multi-agent shielding in environments with general partial observability and develop a novel shielding method which is effective in such domains. Through a series of experiments, we show that agents that use our shielding method are able to safely and successfully solve a variety of RL tasks, including tasks in which prior methods cannot be applied.