LIGHT injects decision-tree-extracted human knowledge into individual intrinsic rewards and improves sparse-reward MARL performance over QMIX, VDN, QTRAN, LIIR, and MASER.
ROMA: Multi-agent reinforcement learning with emergent roles,
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Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning via Incorporating Generalized Human Expertise
LIGHT injects decision-tree-extracted human knowledge into individual intrinsic rewards and improves sparse-reward MARL performance over QMIX, VDN, QTRAN, LIIR, and MASER.