Mastering Complex Control in MOBA Games with Deep Reinforcement Learning（論文筆記）
本文由騰訊AI Lab跟天美髮表，用於王者榮耀1v1的AI訓練，達成99.81%的勝率。文章分了幾個部分進行講解。 大系統 整個框架分爲四個模塊：RL Learner，AI Server，Dispatch module與Memory Pool，如下圖： AI Server：此模塊用當前的agent與遊戲環境進行交互來收集數據，一個AI Server綁定一個cpu，agent會copy到cpu中，爲
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- 1. 論文筆記：《Playing Atari with Deep Reinforcement Learning》
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- 3. 【5分鐘 Paper】Continuous Control With Deep Reinforcement Learning
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相關文章
- 1. 論文筆記：《Playing Atari with Deep Reinforcement Learning》
- 2. Continuous control with Deep Reinforcement Learning
- 3. 【5分鐘 Paper】Continuous Control With Deep Reinforcement Learning
- 4. 論文筆記4：Language Understanding for Text-based Games using Deep Reinforcement Learning
- 5. Playing Atari with Deep Reinforcement Learning
- 6. 論文筆記 Reinforcement Learning with Derivative-Free Exploration
- 7. DDPG，CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING 論文閱讀
- 8. 解讀continuous control with deep reinforcement learning（DDPG）
- 9. 【論文翻譯】Playing Atari with Deep Reinforcement Learning
- 10. 論文筆記——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING