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As the application of artificial intelligence continues to expand, complex decision-making problems such as multi-player gaming, multi-robot planning and multi-vehicle controlling have become new challenges for machine intelligence. MARL which concentrates on learning the optimal strategies of multiple agents that coexist in a shared environment, is a valid method to solve multi-agent decision-making challenges. Among MARL Algorithms, the MAPPO algorithm has won the favor of machine learning community due to its superb performance. However, the original MAPPO algorithm suffers from sparse reward issues. To overcome the sparse rewards problem and achieve sufficient learning in complex task, this paper proposes a IGE-MAPPO which uses a IGM that generates a variable-density and bi-domain reward signal, and conducts experiments on SMAC. The results show that the IGE-MAPPO algorithm can adapt to a variety of complex environment and has improved performance compared with other typical MARL algorithms.

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Multi-agent Reinforcement Learning for Sparse Reward Tasks Using Incremental Goal Enhanced Method

  • 会议论文

  • 发表日期：

    2025年

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  • Minglei Han,
  • Zhentao Guo,
  • Licheng Sun,
  • Ao Ding,
  • Tianhao Wang,
  • Guiyu Zhao,
  • Hongbin Ma

摘要

As the application of artificial intelligence continues to expand, complex decision-making problems such as multi-player gaming, multi-robot planning and multi-vehicle controlling have become new challenges for machine intelligence. MARL which concentrates on learning the optimal strategies of multiple agents that coexist in a shared environment, is a valid method to solve multi-agent decision-making challenges. Among MARL Algorithms, the MAPPO algorithm has won the favor of machine learning community due to its superb performance. However, the original MAPPO algorithm suffers from sparse reward issues. To overcome the sparse rewards problem and achieve sufficient learning in complex task, this paper proposes a IGE-MAPPO which uses a IGM that generates a variable-density and bi-domain reward signal, and conducts experiments on SMAC. The results show that the IGE-MAPPO algorithm can adapt to a variety of complex environment and has improved performance compared with other typical MARL algorithms.