論文筆記：《Playing Atari with Deep Reinforcement Learning》
文章摘要：本文是第一篇使用深度學習和強化學習結合起來的模型的論文。作者採用CNN作爲深度學習模型，結合Q-Learning強化學習算法進行訓練。CNN的輸入爲高維原始像素，輸出爲估計期望獎賞的估值函數。通過本文訓練的神經網絡在7個Atari 2600遊戲上的得分有6個勝過了以往最好方法，有3個遊戲超過了人類專家。 深度學習與強化學習的結合： 一方面，強化學習在視頻和語音方面的主要困難在於，學習
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相關文章
- 1. 【論文翻譯】Playing Atari with Deep Reinforcement Learning
- 2. Playing Atari with Deep Reinforcement Learning
- 3. Playing atari with deep reinforcement learning
- 4. Playing Atari with Deep Reinforcement Learning 中文 講解
- 5. NIPS-2013：Playing Atari with Deep Reinforcement Learning
- 6. Paper reading: Playing Atari with Deep Reinforcement Learning
- 7. 【5分鐘 Paper】Playing Atari with Deep Reinforcement Learning
- 8. RL論文閱讀【一】Playing Atari with Deep Reinforcement Learning
- 9. 論文筆記 Reinforcement Learning with Derivative-Free Exploration
- 10. 【5分鐘 Paper】Continuous Control With Deep Reinforcement Learning