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<p>The non-stationarity problem poses a significant challenge in the fully decentralized cooperative multi-agent reinforcement learning setting, where the environment is inherently unstable, and agents independently learn policies that evolve over time. In this work, we address the non-stationarity challenge by introducing a novel approach integrating pruned prioritized experience replay (PPER) and collaborative learning. Unlike existing methods, PPER ensures that agents selectively retain and utilize relevant experiences, enhancing performance by pruning less significant transitions. Additionally, our approach incorporates collaborative mode, and extends knowledge sharing from training to testing phases, fostering enhanced decision-making. This framework is evaluated in the context of intrusion detection, demonstrating its applicability and potential generalizability to a variety of real-world scenarios. Experimental results on the NSL-KDD dataset demonstrate the effectiveness of our solution, achieving 98.05% accuracy and 97.65% precision.</p>

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Boosting multi-agent reinforcement learning with pruned prioritized experience replay and collaborative learning. Case study: intrusion detection system

  • 期刊论文

  • 发表日期：

    2026年4月13日

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  • Faten Louati,
  • Farah Barika Ktata,
  • Ikram Amous

摘要

The non-stationarity problem poses a significant challenge in the fully decentralized cooperative multi-agent reinforcement learning setting, where the environment is inherently unstable, and agents independently learn policies that evolve over time. In this work, we address the non-stationarity challenge by introducing a novel approach integrating pruned prioritized experience replay (PPER) and collaborative learning. Unlike existing methods, PPER ensures that agents selectively retain and utilize relevant experiences, enhancing performance by pruning less significant transitions. Additionally, our approach incorporates collaborative mode, and extends knowledge sharing from training to testing phases, fostering enhanced decision-making. This framework is evaluated in the context of intrusion detection, demonstrating its applicability and potential generalizability to a variety of real-world scenarios. Experimental results on the NSL-KDD dataset demonstrate the effectiveness of our solution, achieving 98.05% accuracy and 97.65% precision.