  Many cooperative multi-agent problems require agents to learn individual
tasks while contributing to the collective success of the group. This is a
challenging task for current state-of-the-art multi-agent reinforcement
algorithms that are designed to either maximize the global reward of the team
or the individual local rewards. The problem is exacerbated when either of the
rewards is sparse leading to unstable learning. To address this problem, we
present Decomposed Multi-Agent Deep Deterministic Policy Gradient (DE-MADDPG):
a novel cooperative multi-agent reinforcement learning framework that
simultaneously learns to maximize the global and local rewards. We evaluate our
solution on the challenging defensive escort team problem and show that our
solution achieves a significantly better and more stable performance than the
direct adaptation of the MADDPG algorithm.
