加载中...

SUPPORT

觉得内容有帮助？请站长喝杯咖啡 ☕

网站所有内容免费，但运维不免费 😭 AI、服务器、域名……你的支持将用来维持网站正常运行，让这个小站还能继续写下去~

微信赞助收款二维码

打开微信扫一扫 · 金额随意

首页知识库教程工具博客资讯术语表面试题库关于?个人中心

← 返回课程

The Hugging Face Deep Reinforcement Learning Course 🤗 (v2.0)

95 / 114·markdown

第 0 单元

01Discord 101
02Welcome to the 🤗 Deep Reinforcement Learning Course
03Setup

第 1 单元

04Additional Readings
05Conclusion
06The “Deep” in Reinforcement Learning
07The Exploration/Exploitation trade-off
08Glossary
09Train your first Deep Reinforcement Learning Agent 🤖
10Introduction to Deep Reinforcement Learning
11Quiz
12The Reinforcement Learning Framework
13Summary
14Type of tasks
15Two main approaches for solving RL problems
16What is Reinforcement Learning?

第 2 单元

17Additional Readings
18The Bellman Equation: simplify our value estimation
19Conclusion
20Glossary
21Hands-on
22Introduction to Q-Learning
23Monte Carlo vs Temporal Difference Learning
24Mid-way Quiz
25Mid-way Recap
26A Q-Learning example
27Q-Learning Recap
28Introducing Q-Learning
29Second Quiz
30Two types of value-based methods
31What is RL? A short recap

第 3 单元

32Additional Readings
33Conclusion
34The Deep Q-Learning Algorithm
35The Deep Q-Network (DQN)
36From Q-Learning to Deep Q-Learning
37Glossary
38Hands-on
39Deep Q-Learning
40Quiz

第 4 单元

41Additional Readings
42The advantages and disadvantages of policy-gradient methods
43Conclusion
44Glossary
45Hands on
46Introduction
47(Optional) the Policy Gradient Theorem
48Diving deeper into policy-gradient methods
49Quiz
50What are the policy-based methods?

第 5 单元

51Bonus: Learn to create your own environments with Unity and MLAgents
52Conclusion
53(Optional) What is Curiosity in Deep Reinforcement Learning?
54Hands-on
55How do Unity ML-Agents work?
56An Introduction to Unity ML-Agents
57The Pyramid environment
58Quiz
59The SnowballTarget Environment

第 6 单元

60Additional Readings
61Advantage Actor-Critic (A2C)
62Conclusion
63Advantage Actor Critic (A2C) using Robotics Simulations with Panda-Gym 🤖
64Introduction
65Quiz
66The Problem of Variance in Reinforce

第 7 单元

67Additional Readings
68Conclusion
69Hands-on
70An introduction to Multi-Agents Reinforcement Learning (MARL)
71Introduction
72Designing Multi-Agents systems
73Quiz
74Self-Play: a classic technique to train competitive agents in adversarial games

第 8 单元

75Additional Readings
76Introducing the Clipped Surrogate Objective Function
77Conclusion
78Conclusion
79Hands-on
80Hands-on: advanced Deep Reinforcement Learning. Using Sample Factory to play Doom from pixels
81Introduction to PPO with Sample-Factory
82Introduction
83The intuition behind PPO
84Visualize the Clipped Surrogate Objective Function

其他

85The certification process
86Congratulations
87Live 1: How the course work, Q&A, and playing with Huggy
88Conclusion
89How Huggy works
90Introduction
91Play with Huggy
92Let's train and play with Huggy 🐶
93Hands-on
94Introduction
95Optuna Tutorial
96(Automatic) Curriculum Learning for RL
97Decision Transformers
98Interesting Environments to try
99Generalization in Reinforcement Learning
100Godot RL Agents
101Introduction
102Language models in RL
103An Introduction to Unreal Learning Agents
104Model Based Reinforcement Learning (MBRL)
105Offline vs. Online Reinforcement Learning
106Brief introduction to RL documentation
107RLHF
108Student Works
109Conclusion:
110(Optional) How to customize the environment
111Getting started:
112Introduction:
113The environment
114Train our robot

Chapter 95

Optuna Tutorial

Optuna Tutorial

The content below comes from Antonin's Raffin ICRA 2022 presentations, he's one of the founders of Stable-Baselines and RL-Baselines3-Zoo.

The theory behind Hyperparameter tuning

Optuna Tutorial

The notebook 👉 here

上一章

Introduction

下一章

(Automatic) Curriculum Learning for RL

本页镜像自上游仓库，固定版本 ad165751。许可证：Apache License 2.0。原文只读，笔记与划线保存在你的助记词身份下。

阅读进度

0%

整课进度0/114 章 · 0%

快捷键：←/→ 换章 · C 完成 · F 专注 · Esc 退出

阅读设置

字号

行宽

行距

本章笔记

选中正文可划线或写笔记。随助记词身份同步，仅自己可见。

暂无笔记。选中一段文字试试。

本章导航

The theory behind Hyperparameter tuning
Optuna Tutorial

中文 AI 学习与实践平台。知识库、教程、工具与资讯集中一站，帮你把人工智能真正学到手。

联系support.aimaster.cc@gmail.com

意见反馈 · 内容纠错 · 需求交流

探索

知识库
教程
工具
博客
资讯
术语表
面试题库
关于我们
隐私政策
服务条款

专题

精选教程
机器学习
深度学习
自然语言处理
计算机视觉

© 2026 AI Master. All rights reserved.

隐私政策·服务条款·关于我们·精通人工智能 · ai-master.cc