Playing atari with deep reinforcement learning

时间 2020-12-24

标签 DRL 繁體版

原文原文链接

Introduction 传统RL手动提取选择特征，DL(CNN)可以根据raw sensory的数据抽象出一些high-level特征，DRL的困难： DL训练使用大量的labeled数据，而RL数据量小，且reward和action之间的delay DL样本间独立，RL样本间相关 RL样本的分布随着学习改变，DL中设定的固定的分布本文针对2，3困难的方法： experience replay

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1. Playing Atari with Deep Reinforcement Learning
2. NIPS-2013：Playing Atari with Deep Reinforcement Learning
3. Paper reading: Playing Atari with Deep Reinforcement Learning
4. 【5分钟 Paper】Playing Atari with Deep Reinforcement Learning
5. Playing Atari with Deep Reinforcement Learning 中文讲解
6. 论文笔记：《Playing Atari with Deep Reinforcement Learning》
7. 【论文翻译】Playing Atari with Deep Reinforcement Learning
8. RL论文阅读【一】Playing Atari with Deep Reinforcement Learning
9. 【5分钟 Paper】Continuous Control With Deep Reinforcement Learning
10. Continuous control with Deep Reinforcement Learning
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1. Playing Atari with Deep Reinforcement Learning
2. NIPS-2013：Playing Atari with Deep Reinforcement Learning
3. Paper reading: Playing Atari with Deep Reinforcement Learning
4. 【5分钟 Paper】Playing Atari with Deep Reinforcement Learning
5. Playing Atari with Deep Reinforcement Learning 中文讲解
6. 论文笔记：《Playing Atari with Deep Reinforcement Learning》
7. 【论文翻译】Playing Atari with Deep Reinforcement Learning
8. RL论文阅读【一】Playing Atari with Deep Reinforcement Learning
9. 【5分钟 Paper】Continuous Control With Deep Reinforcement Learning
10. Continuous control with Deep Reinforcement Learning

>>更多相关文章<<