從Playing Atari with Deep Reinforcement Learning 看神經網絡的輸入，學習的狀態空間
Introduction：學習直接從高維的感觀輸入（視覺，語音）去直接控制智能體是強化學習一個長期的挑戰。一些成功的RL應用都是依賴於hand-crafted的特徵。最近深度學習的進步使提取高水平的特徵成爲了可能。 參考傳送門：https://blog.csdn.net/qq_34718684/article/details/89386302 Playing Atari with Deep Re
相關文章
- 1. Playing atari with deep reinforcement learning
- 2. Playing Atari with Deep Reinforcement Learning
- 3. 論文筆記：《Playing Atari with Deep Reinforcement Learning》
- 4. NIPS-2013：Playing Atari with Deep Reinforcement Learning
- 5. 【論文翻譯】Playing Atari with Deep Reinforcement Learning
- 6. 【5分鐘 Paper】Playing Atari with Deep Reinforcement Learning
- 7. Playing Atari with Deep Reinforcement Learning 中文 講解
- 8. Paper reading: Playing Atari with Deep Reinforcement Learning
- 9. RL論文閱讀【一】Playing Atari with Deep Reinforcement Learning
- 10. Continuous control with Deep Reinforcement Learning
- 更多相關文章...
- • C# 文件的輸入與輸出 - C#教程
- • 持久化對象的狀態及狀態轉換 - Hibernate教程
- • 適用於PHP初學者的學習線路和建議
- • 互聯網組織的未來：剖析GitHub員工的任性之源
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。
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. Playing Atari with Deep Reinforcement Learning
- 3. 論文筆記：《Playing Atari with Deep Reinforcement Learning》
- 4. NIPS-2013：Playing Atari with Deep Reinforcement Learning
- 5. 【論文翻譯】Playing Atari with Deep Reinforcement Learning
- 6. 【5分鐘 Paper】Playing Atari with Deep Reinforcement Learning
- 7. Playing Atari with Deep Reinforcement Learning 中文 講解
- 8. Paper reading: Playing Atari with Deep Reinforcement Learning
- 9. RL論文閱讀【一】Playing Atari with Deep Reinforcement Learning
- 10. Continuous control with Deep Reinforcement Learning