Focusing Influence Mechanism for Multi-Agent Reinforcement Learning
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866913113126731776 |
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| author | Park, Yisak Lee, Sunwoo Han, Seungyul |
| author_facet | Park, Yisak Lee, Sunwoo Han, Seungyul |
| contents | Cooperative multi-agent reinforcement learning (MARL) under sparse rewards remains fundamentally challenging because agents often fail to concentrate their influence, leading to insufficiently coordinated exploration. To address this, we propose the Focusing Influence Mechanism (FIM), a framework that encourages agents to focus their influence on under-explored parts of the state space through an entropy-based criterion, while leveraging eligibility traces to enable multiple agents to consistently align and sustain their influence on the same parts of the state space when beneficial, thereby promoting coordinated and persistent joint behavior. By emphasizing under-explored regions of the state space, FIM facilitates more efficient and structured exploration even under extremely sparse rewards. Across diverse MARL benchmarks, FIM consistently improves cooperative performance over strong baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19417 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Focusing Influence Mechanism for Multi-Agent Reinforcement Learning Park, Yisak Lee, Sunwoo Han, Seungyul Machine Learning Multiagent Systems Cooperative multi-agent reinforcement learning (MARL) under sparse rewards remains fundamentally challenging because agents often fail to concentrate their influence, leading to insufficiently coordinated exploration. To address this, we propose the Focusing Influence Mechanism (FIM), a framework that encourages agents to focus their influence on under-explored parts of the state space through an entropy-based criterion, while leveraging eligibility traces to enable multiple agents to consistently align and sustain their influence on the same parts of the state space when beneficial, thereby promoting coordinated and persistent joint behavior. By emphasizing under-explored regions of the state space, FIM facilitates more efficient and structured exploration even under extremely sparse rewards. Across diverse MARL benchmarks, FIM consistently improves cooperative performance over strong baselines. |
| title | Focusing Influence Mechanism for Multi-Agent Reinforcement Learning |
| topic | Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2506.19417 |