Actor-Attention-Critic for Multi-Agent Reinforcement Learning論文學習筆記
論文鏈接：Actor-Attention-Critic for Multi-Agent Reinforcement Learning 目錄） 一.改進算法的核心內容 i.Attention機制 ii.反事實基線 iii.交叉熵 二.實驗部分 最近學習了ICML2019的一篇多智能體強化學習的文章，感覺想法很新穎，所以記錄一下學習時候的筆記，方便以後查閱。文中有不恰當或者有問題的地方，歡迎在
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相關文章
- 1. 閱讀Qatten：A General Framework for Cooperative Multiagent Reinforcement Learning
- 2. 論文筆記 Reinforcement Learning with Derivative-Free Exploration
- 3. 論文筆記 Benchmarking Model-Based Reinforcement Learning
- 4. 論文筆記——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 5. 論文筆記《Neural Architecture Search With Reinforcement Learning》
- 6. 【論文筆記】NEURAL COMBINATORIAL OPTIMIZATION WITH REINFORCEMENT LEARNING
- 7. 論文筆記：《Playing Atari with Deep Reinforcement Learning》
- 8. 強化學習論文筆記：Real-Time Reinforcement Learning
- 9. 論文筆記4：Language Understanding for Text-based Games using Deep Reinforcement Learning
- 10. 論文閱讀筆記《Ranking Sentences for Extractive Summarization with Reinforcement Learning》