Continuous control with Deep Reinforcement Learning
來源：ICLR2016 作者：Deepmind 創新點：將Deep Q-Learning應用到連續動作領域continuous control（比如機器人控制） 實驗成果：能夠魯棒地解決20個仿真的物理控制任務，包含機器人的操作，運動，開車。。。效果比肩傳統的規劃方法。 優點： End-to-End 將Deep Reinforcement Learning應用在連續動作空間 缺點： 對於3D賽車，
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相關文章
- 1. 解讀continuous control with deep reinforcement learning（DDPG）
- 2. 【5分鐘 Paper】Continuous Control With Deep Reinforcement Learning
- 3. DDPG，CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING 論文閱讀
- 4. PR17.10.2:Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
- 5. Generating Text with Deep Reinforcement Learning
- 6. Playing Atari with Deep Reinforcement Learning
- 7. Playing atari with deep reinforcement learning
- 8. Deep Reinforcement Learning
- 9. Deep Reinforcement Learning with Iterative Shift for Visual Tracking
- 10. Mastering Complex Control in MOBA Games with Deep Reinforcement Learning（論文筆記）