解讀continuous control with deep reinforcement learning（DDPG）
版權聲明：本文爲博主原創文章，未經博主允許不得轉載。 博主：shenshikexmu 聯繫方式：[email protected] 緣起 DDPG，是Google Deepmind第一篇關於連續動作的深度加強學習論文（是否第一篇存疑）。DQN（Deep Q Network）生成的策略執行的動作是離散或者低維的，雖然在狀態輸入上可以是高維的觀察狀態。如在DQN2014中，有效的動作在4到18個之間，
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相關文章
- 1. Continuous control with Deep Reinforcement Learning
- 2. DDPG，CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING 論文閱讀
- 3. 【5分鐘 Paper】Continuous Control With Deep Reinforcement Learning
- 4. PR17.10.2:Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
- 5. Deep Reinforcement Learning - DDPG原理和算法
- 6. Deep Reinforcement Learning - 1. DDPG原理和算法
- 7. Deep Reinforcement Learning —— DDPG原理和算法
- 8. Generating Text with Deep Reinforcement Learning
- 9. Deep Reinforcement Learning
- 10. Playing atari with deep reinforcement learning