Playing atari with deep reinforcement learning
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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相关文章
- 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