Enhancing Multi-Agent Reinforcement Learning via Knowledge-Embedded Modular Framework for Online Basketball Games
High sample complexity presents a major challenge in applying multi-agent reinforcement learning (MARL) to dynamic, high-dimensional sports such as basketball. To address this problem, we proposed the knowledge-embedded modular framework (KEMF), which partitions the environment into offense, defense...
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| 主要な著者: | , , |
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| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
MDPI AG
2026-01-01
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| シリーズ: | Mathematics |
| 主題: | |
| オンライン･アクセス: | https://www.mdpi.com/2227-7390/14/3/419 |
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