解读continuous control with deep reinforcement learning（DDPG）
版权声明：本文为博主原创文章，未经博主允许不得转载。 博主：shenshikexmu 联系方式：[email protected] 缘起 DDPG，是Google Deepmind第一篇关于连续动作的深度加强学习论文（是否第一篇存疑）。DQN（Deep Q Network）生成的策略执行的动作是离散或者低维的，虽然在状态输入上可以是高维的观察状态。如在DQN2014中，有效的动作在4到18个之间，
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相关文章
- 1. Continuous control with Deep Reinforcement Learning
- 2. DDPG，CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING 论文阅读
- 3. 【5分钟 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