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

Multi-Agent Reinforcement Learning for Problems with Combined Individual and Team Reward