论文笔记6：Increasing the Action Gap: New Operators for Reinforcement Learning
参考文献：New Operators for Reinforcement Learning 同名知乎：uuummmmiiii 这篇文章实在是式子多，整个看懵，网上目前没啥人看过这篇，论文有两部分，我挣扎了一下看了第一部分，所以第二部分具体作者创新了什么，做了什么相关推导我也不知道，哭泣。 如有错误还请指出，本人小白，希望帮助更多的人，一同进步。 论文分为两部分：前部分：作者介绍新提出的新算子。 后
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相关文章
- 1. 论文笔记之：Asynchronous Methods for Deep Reinforcement Learning
- 2. 论文笔记 Reinforcement Learning with Derivative-Free Exploration
- 3. 论文笔记：《Playing Atari with Deep Reinforcement Learning》
- 4. 【论文笔记】Deep Reinforcement Learning for Page-wise Recommendations
- 5. CVPR 2017 ADNet:《 Action-Decision Networks for Visual Tracking with Deep Reinforcement Learning》论文笔记
- 6. 论文笔记 Benchmarking Model-Based Reinforcement Learning
- 7. 论文笔记 Retrospective Analysis of the 2019MineRL Competition on Sample Efficient Reinforcement Learning
- 8. 论文笔记：Learning how to Active Learn: A Deep Reinforcement Learning Approach
- 9. 论文笔记4：Language Understanding for Text-based Games using Deep Reinforcement Learning
- 10. 论文笔记——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING