Value-Decomposition Networks For Cooperative Multi-Agent Learning筆記
Value-Decomposition Networks For Cooperative Multi-Agent Learning 1. 論文講了什麼/主要貢獻是什麼 多代理強化學習任務的解決方案存在完全集中，和獨立學習兩種方案。將二者結合，得到了集中學習、分散決策的值分解網絡，並利用已有技術如DQN架構、目標網絡、經驗回放、Dueling網絡以及面向部分可觀測環境下采用的RNN網絡，設計出值分解
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相關文章
- 1. 閱讀Qatten：A General Framework for Cooperative Multiagent Reinforcement Learning
- 2. neuron networks for machine learning coursera課程筆記
- 3. Learning Convolutional Neural Networks for Graphs論文筆記
- 4. 論文筆記：Prototypical Networks for Few-shot Learning
- 5. Matching Networks for one Shot Learning 閱讀筆記
- 6. FeUdal Networks for Hierarchical Reinforcement Learning 閱讀筆記
- 7. Learning Policy Representations in Multiagent Systems
- 8. Machine Learning for Communication Networks
- 9. Cooperative Cognitive Radio Networks，Coalitional Game Formulation
- 10. Neural Networks and Deep Learning 【編程筆記】