Playing atari with deep reinforcement learning
Introduction 傳統RL手動提取選擇特徵，DL(CNN)可以根據raw sensory的數據抽象出一些high-level特徵，DRL的困難： DL訓練使用大量的labeled數據，而RL數據量小，且reward和action之間的delay DL樣本間獨立，RL樣本間相關 RL樣本的分佈隨着學習改變，DL中設定的固定的分佈 本文針對2，3困難的方法： experience replay
相關文章
- 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分鐘 Paper】Playing Atari with Deep Reinforcement Learning
- 5. Playing Atari with Deep Reinforcement Learning 中文 講解
- 6. 論文筆記：《Playing Atari with Deep Reinforcement Learning》
- 7. 【論文翻譯】Playing Atari with Deep Reinforcement Learning
- 8. RL論文閱讀【一】Playing Atari with Deep Reinforcement Learning
- 9. 【5分鐘 Paper】Continuous Control With Deep Reinforcement Learning
- 10. Continuous control with Deep Reinforcement Learning
- 更多相關文章...
- • XSLT
- • XQuery 添加元素 和屬性 - XQuery 教程
- • Java Agent入門實戰（一）-Instrumentation介紹與使用
- • Java Agent入門實戰（三）-JVM Attach原理與使用
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 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分鐘 Paper】Playing Atari with Deep Reinforcement Learning
- 5. Playing Atari with Deep Reinforcement Learning 中文 講解
- 6. 論文筆記：《Playing Atari with Deep Reinforcement Learning》
- 7. 【論文翻譯】Playing Atari with Deep Reinforcement Learning
- 8. RL論文閱讀【一】Playing Atari with Deep Reinforcement Learning
- 9. 【5分鐘 Paper】Continuous Control With Deep Reinforcement Learning
- 10. Continuous control with Deep Reinforcement Learning