<ul><li>Recently, deep Multi-Agent Reinforcement Learning (MARL) has demonstrated its potential to tackle complex cooperative tasks, pushing the boundaries of AI in collaborative environments.</li><li>To enhance MARL performance, a novel sample reuse approach called Multi-Agent Novelty-Guided sample Reuse (MANGER) is introduced.</li><li>MANGER utilizes a Random Network Distillation (RND) network to measure the novelty of each agent's current state and assigns additional sample update opportunities based on the uniqueness of the data.</li><li>Evaluations show significant improvements in MARL effectiveness in scenarios such as Google Research Football and StarCraft II micromanagement tasks.</li></ul>

Novelty-Guided Data Reuse for Efficient and Diversified Multi-Agent Reinforcement Learning

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