  Multi-Agent Reinforcement Learning (MARL) algorithms show amazing performance
in simulation in recent years, but placing MARL in real-world applications may
suffer safety problems. MARL with centralized shields was proposed and verified
in safety games recently. However, centralized shielding approaches can be
infeasible in several real-world multi-agent applications that involve
non-cooperative agents or communication delay. Thus, we propose to combine MARL
with decentralized Control Barrier Function (CBF) shields based on available
local information. We establish a safe MARL framework with decentralized
multiple CBFs and develop Multi-Agent Deep Deterministic Policy Gradient
(MADDPG) to Multi-Agent Deep Deterministic Policy Gradient with decentralized
multiple Control Barrier Functions (MADDPG-CBF). Based on a collision-avoidance
problem that includes not only cooperative agents but obstacles, we demonstrate
the construction of multiple CBFs with safety guarantees in theory. Experiments
are conducted and experiment results verify that the proposed safe MARL
framework can guarantee the safety of agents included in MARL.
