論文筆記6：Increasing the Action Gap: New Operators for Reinforcement Learning
參考文獻：New Operators for Reinforcement Learning 同名知乎：uuummmmiiii 這篇文章實在是式子多，整個看懵，網上目前沒啥人看過這篇，論文有兩部分，我掙扎了一下看了第一部分，所以第二部分具體作者創新了什麼，做了什麼相關推導我也不知道，哭泣。 如有錯誤還請指出，本人小白，希望幫助更多的人，一同進步。 論文分爲兩部分：前部分：作者介紹新提出的新算子。 後
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相關文章
- 1. 論文筆記之：Asynchronous Methods for Deep Reinforcement Learning
- 2. 論文筆記 Reinforcement Learning with Derivative-Free Exploration
- 3. 論文筆記：《Playing Atari with Deep Reinforcement Learning》
- 4. 【論文筆記】Deep Reinforcement Learning for Page-wise Recommendations
- 5. CVPR 2017 ADNet:《 Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning》論文筆記
- 6. 論文筆記 Benchmarking Model-Based Reinforcement Learning
- 7. 論文筆記 Retrospective Analysis of the 2019MineRL Competition on Sample Efficient Reinforcement Learning
- 8. 論文筆記：Learning how to Active Learn: A Deep Reinforcement Learning Approach
- 9. 論文筆記4：Language Understanding for Text-based Games using Deep Reinforcement Learning
- 10. 論文筆記——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING