  Multi-agent reinforcement learning (MARL) has been increasingly used in a
wide range of safety-critical applications, which require guaranteed safety
(e.g., no unsafe states are ever visited) during the learning
process.Unfortunately, current MARL methods do not have safety guarantees.
Therefore, we present two shielding approaches for safe MARL. In centralized
shielding, we synthesize a single shield to monitor all agents' joint actions
and correct any unsafe action if necessary. In factored shielding, we
synthesize multiple shields based on a factorization of the joint state space
observed by all agents; the set of shields monitors agents concurrently and
each shield is only responsible for a subset of agents at each
step.Experimental results show that both approaches can guarantee the safety of
agents during learning without compromising the quality of learned policies;
moreover, factored shielding is more scalable in the number of agents than
centralized shielding.
