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This is a private learning repository for reinforcement learning techniques used in robotics.
| Date | Stars |
|---|---|
| 2026-07-31 | 516 |
| 2026-08-02 | 517 |
| 2026-08-03 | 517 |
| 2026-08-04 | 517 |
| 2026-08-05 | 518 |
| 2026-08-06 | 518 |
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#! https://zhuanlan.zhihu.com/p/143392167  # **如需转发，烦请邮件告知 [[email protected]]([email protected])** # Reinforcement-Learning-in-Robotics Content 专栏目录 This is a private learning repository about **R**einforcement learning techniques, **R**easoning, and **R**epresentation learning used in **R**obotics, founded for **Real intelligence**. ## Reinforcement Learning Foundation 1. **神经网络基础**：反向传播推导与卷积公式 [[Zhihu](https://zhuanlan.zhihu.com/p/114370969)] 2. **强化学习基础 Ⅰ**：马尔可夫与值函数 [[Zhihu](https://zhuanlan.zhihu.com/p/114377860)] 3. **强化学习基础 Ⅱ**：动态规划，蒙特卡洛，时序差分 [[Zhihu](https://zhuanlan.zhihu.com/p/114482584)] 4. **强化学习基础 Ⅲ**：on-policy, off-policy & Model-based, Model-free & Rollout [[Zhihu](https://zhuanlan.zhihu.com/p/115629505)] 5. **强化学习基础 Ⅳ**：State-of-the-art 强化学习经典算法汇总 [[Zhihu](https://zhuanlan.zhihu.com/p/137208923)] 6. **强化学习基础 Ⅴ**：Q learning 原理与实战 [[Zhihu](https://zhuanlan.zhihu.com/p/141267943)] 7. **强化学习基础 Ⅵ**：DQN 原理与实战 [[Zhihu](https://zhuanlan.zhihu.com/p/141268549)] 8. **强化学习基础 Ⅶ**：Double DQN & Dueling DQN 原理与实战 [[Zhihu](https://zhuanlan.zhihu.com/p/141268851)] 9. **强化学习基础 Ⅷ**：Vanilla Policy Gradient 策略梯度原理与实现 [[Zhihu](https://zhuanlan.zhihu.com/p/141269134)] 10. **强化学习基础 Ⅸ**：一文读懂 TRPO 原理与实现 [[Zhihu](https://zhuanlan.zhihu.com/p/141269503)] 11. **强化学习基础 Ⅹ**：一文读懂两种 PPO 原理与实现 [[Zhihu](https://zhuanlan.zhihu.com/p/141269918)] 12. **强化学习基础 Ⅺ**： Actor-Critic & A2C 原理与实现 [[Zhihu](https://zhuanlan.zhihu.com/p/145168493)] 13. **强化学习基础 Ⅻ**：DDPG 原理与实现 [[Zhihu](https://zhuanlan.zhihu.com/p/145181679)] 14. **强化学习基础 XIII**：Twin Delayed DDPG TD3原理与实现 [[Zhihu](https://zhuanlan.zhihu.com/p/145621630)] ## Model-based RL 1. **Model-Based RL Ⅰ**：Dyna, MVE & STEVE [[Zhihu](https://zhuanlan.zhihu.com/p/102197348)] 2. **Model-Based RL Ⅱ**：MBPO原理解读 [[Zhihu](https://zhuanlan.zhihu.com/p/105645139)] 3. **Model-Based RL Ⅲ**：从源码读懂PILCO [[Zhihu](https://zhuanlan.zhihu.com/p/138337983)] ## Probabilistic in Robotics 1. **PR 序**：机器人学的概率方法学习路径 [[Zhihu](https://zhuanlan.zhihu.com/p/150563142)] 2. **PR Ⅰ**：最大似然估计MLE与最大后验概率估计MAP [[Zhihu](https://zhuanlan.zhihu.com/p/138608823)] 3. **PR Ⅱ**：贝叶斯估计/推断及其与MAP的区别 [[Zhihu](https://zhuanlan.zhihu.com/p/139480748)] 4. **PR Ⅲ**：从高斯分布到高斯过程、高斯过程回归、贝叶斯优化 [[Zhihu](https://zhuanlan.zhihu.com/p/139478368)] 5. **PR Ⅳ**：贝叶斯神经网络 Bayesian Neural Network [[Zhihu](https://zhuanlan.zhihu.com/p/139523520)] 6. **PR Ⅴ**：熵、KL散度、交叉熵、JS散度及python实现 [[Zhihu](https://zhuanlan.zhihu.com/p/143105854)] 7. **PR Ⅵ**：多元连续高斯分布的KL散度及python实现 [[Zhihu](https://zhuanlan.zhihu.com/p/143124676)] 8. **PR Sampling Ⅰ**：蒙特卡洛采样、重要性采样及python实现 [[Zhihu](https://zhuanlan.zhihu.com/p/150693309)] 9. **PR Sampling Ⅱ**：马尔可夫链蒙特卡洛 MCMC及python实现 [[Zhihu](https://zhuanlan.zhihu.com/p/150742395)] 10. **PR Sampling Ⅲ**：M-H and Gibbs 采样 [[Zhihu](https://zhuanlan.zhihu.com/p/150946559)] 11. **PR Structured Ⅰ**：Graph Neural Network: An Introduction Ⅰ [[Zhihu](https://zhuanlan.zhihu.com/p/158984343)] 12. **PR Structured Ⅱ**：Structured Probabilistic Model 结构化概率模型 [[Zhihu](https://zhuanlan.zhihu.com/p/161703636)] 13. **PR Structured Ⅲ**：马尔可夫、隐马尔可夫 HMM 、条件随机场 CRF 全解析及其python实现 [[Zhihu](https://zhuanlan.zhihu.com/p/259660645)] 14. **PR Structured Ⅳ**：General / Graph Conditional Random Field (CRF) 及其 python 实现 [[Zhihu](https://zhuanlan.zhihu.com/p/259883878)] 15. **PR Structured Ⅴ**：GraphRNN——将图生成问题转化为序列生成 [[Zhihu](https://zhuanlan.zhihu.com/p/276873641)] 16. **PR Reasoning 序**：Reasoning Robotics 推理机器人学习路线与资源汇总 [[Zhihu](https://zhuanlan.zhihu.com/p/262568794)] 17. **PR Reasoning Ⅰ**：Bandit问题与 UCB / UCT / AlphaGo [[Zhihu](https://zhuanlan.zhihu.com/p/218398647)] 18. **PR Reasoning Ⅱ**：Relational Inductive bias 关系归纳偏置及其在深度学习中的应用 [[Zhihu](https://zhuanlan.zhihu.com/p/261081574)] 19. **PR Reasoning Ⅲ**：基于图表征的关系推理框架 —— Graph Network [[Zhihu](https://zhuanlan.zhihu.com/p/261127145)] 20. **PR Reasoning Ⅳ**：数理逻辑（命题逻辑、谓词逻辑）知识整理 [[Zhihu](https://zhuanlan.zhihu.com/p/262984951)] 21. **PR Memory Ⅰ**：Mem
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Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:db2d61e83f30e399, name:reinforcement learning, desc:reinforcement learning
matched fp:db2d61e83f30e399, name:robotics, desc:robotics