【論文翻譯】Playing Atari with Deep Reinforcement Learning
摘要：我們第一個提出了利用強化學習成功地從高維輸入中直接學習控制策略的深度學習模型。該模型是一個卷積神經網絡，經過Q-learning的、訓練，輸入爲原始像素，輸出爲用來估計未來reward的價值函數。我們將我們的方法應用於遊戲廳學習環境下的7款atari 2600遊戲，沒有調整過架構或學習算法。我們發現它在6個遊戲中超越了所有以前的方法，並且在3個遊戲中超過了人類專家。 1 . Introduc
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- 3. Playing atari with deep reinforcement learning
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- 5. NIPS-2013：Playing Atari with Deep Reinforcement Learning
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相關文章
- 1. 論文筆記：《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. 【5分鐘 Paper】Continuous Control With Deep Reinforcement Learning
- 10. 論文翻譯：Deep Progressive Reinforcement Learning for Skeleton-based Action Recognition