  Offline reinforcement learning (RL) has received considerable attention in
recent years due to its attractive capability of learning policies from offline
datasets without environmental interactions. Despite some success in the
single-agent setting, offline multi-agent RL (MARL) remains to be a challenge.
The large joint state-action space and the coupled multi-agent behaviors pose
extra complexities for offline policy optimization. Most existing offline MARL
studies simply apply offline data-related regularizations on individual agents,
without fully considering the multi-agent system at the global level. In this
work, we present OMIGA, a new offline m ulti-agent RL algorithm with implicit
global-to-local v alue regularization. OMIGA provides a principled framework to
convert global-level value regularization into equivalent implicit local value
regularizations and simultaneously enables in-sample learning, thus elegantly
bridging multi-agent value decomposition and policy learning with offline
regularizations. Based on comprehensive experiments on the offline multi-agent
MuJoCo and StarCraft II micro-management tasks, we show that OMIGA achieves
superior performance over the state-of-the-art offline MARL methods in almost
all tasks.
