Continuous control with deep reinforcement learning
来源：互联网 发布：淘宝小号批发一手货源 编辑：程序博客网 时间：2024/05/17 23:40
https://arxiv.org/abs/1509.02971
(Submitted on 9 Sep 2015 (v1), last revised 29 Feb 2016 (this version, v5))
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies end-to-end: directly from raw pixel inputs.
Submission history
From: Jonathan Hunt [view email][v1] Wed, 9 Sep 2015 23:01:36 GMT (344kb,D)
[v2] Wed, 18 Nov 2015 17:34:41 GMT (338kb,D)
[v3] Thu, 7 Jan 2016 19:09:07 GMT (338kb,D)
[v4] Tue, 19 Jan 2016 20:30:47 GMT (339kb,D)
[v5] Mon, 29 Feb 2016 18:45:53 GMT (339kb,D)
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)
0 0
- Continuous control with deep reinforcement learning
- Continuous control with Deep Reinforcement Learning
- Paper Reading 3:Continuous control with Deep Reinforcement Learning
- 解读continuous control with deep reinforcement learning（DDPG）
- 解读continuous control with deep reinforcement learning（DDPG）
- Continuous control with Deep Reinforcement Learning与DDPG（Deep Deterministic Policy Gradient）的理解
- Continuous control with deep reinforcement learning(DDPG，深度确定策略梯度)练习
- DRL前沿之：Benchmarking Deep Reinforcement Learning for Continuous Control
- PR17.10.2:Reproducibility of Benchmarked Deep Reinforcement Learning Tasks for Continuous Control
- Playing Atari with Deep Reinforcement Learning
- Playing Atari with Deep Reinforcement Learning
- learning to communicate with deep multi-agent reinforcement learning
- Paper Reading 2:Human-level control through deep reinforcement learning
- Human-level control through deep reinforcement learning(中文翻译)
- DQN-《Human-level control through deep reinforcement learning》译文
- 笔记——“Human-level control through deep reinforcement learning”
- Deep Reinforcement Learning 基础知识
- Deep Reinforcement Learning
- getSession()和getCurrentSession()的区别及常见问题
- 导入一个AndroidStudio工程作为一个Library Module
- vue-cli+webpack引入jQuery
- 设计模式-装饰模式
- rootframe 为什么空格是红色的
- Continuous control with deep reinforcement learning
- C++实验4：项目6—输出星号图
- 如何用脚本来控制角色动作
- 从ZonedDateTime开启日期时间的管理
- HTML5基础加强css样式篇（table-cell应用，多列等宽）（五十）
- JavaScript倒计时
- c++第四次上机实验
- c++实验4-项目6
- java对象拷贝之BeanUtils.copyProperties() 用法
