Tailoring knowledge for empowered cooperative actions in multi-agent reinforcement learning

Behavioral diversity emerges as a crucial factor for achieving effective collaboration in Multi-Agent Reinforcement Learning (MARL). Current methods often use partial parameter sharing, such as sharing the same representation layer, to balance behavioral diversity and algorithmic scalability. However, this approach ignores that different agents need different decision knowledge, causing training conflicts and knowledge redundancy. To solve these, we propose Tailoring Knowledge for Empowered Cooperative Actions in Multi-Agent Reinforcement Learning (TKCA). Specially, we employ a set of Knowledge Encoders to encode different environment types of knowledge and utilize a Knowledge Selector network to assist each agent in decision-making by selecting the corresponding knowledge. We evaluated TKCA in challenging StarCraftII micromanagement games and Google Research Football games, and the results demonstrate the superior performance of TKCA.