Continuous control with Deep Reinforcement Learning

时间 2020-12-24

标签人工智能繁體版

原文原文链接

来源：ICLR2016 作者：Deepmind 创新点：将Deep Q-Learning应用到连续动作领域continuous control（比如机器人控制）实验成果：能够鲁棒地解决20个仿真的物理控制任务，包含机器人的操作，运动，开车。。。效果比肩传统的规划方法。优点： End-to-End 将Deep Reinforcement Learning应用在连续动作空间缺点：对于3D赛车，

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1. 解读continuous control with deep reinforcement learning（DDPG）
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3. DDPG，CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING 论文阅读
4. PR17.10.2:Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
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1. 解读continuous control with deep reinforcement learning（DDPG）
2. 【5分钟 Paper】Continuous Control With Deep Reinforcement Learning
3. DDPG，CONTINUOUS CONTROL WITH DEEP REINFORCEMENT LEARNING 论文阅读
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（论文笔记）

>>更多相关文章<<