课程信息
4.3
114 个评分
33 个审阅
专项课程
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100% 在线

立即开始,按照自己的计划学习。
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高级

高级

完成时间(小时)

完成时间大约为39 小时

建议:6 weeks of study, 3-6 hours/week for base track, 6-9 with all the horrors of honors section...
可选语言

英语(English)

字幕:英语(English)
专项课程
100% 在线

100% 在线

立即开始,按照自己的计划学习。
可灵活调整截止日期

可灵活调整截止日期

根据您的日程表重置截止日期。
高级

高级

完成时间(小时)

完成时间大约为39 小时

建议:6 weeks of study, 3-6 hours/week for base track, 6-9 with all the horrors of honors section...
可选语言

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1
完成时间(小时)
完成时间为 5 小时

Intro: why should i care?

In this module we gonna define and "taste" what reinforcement learning is about. We'll also learn one simple algorithm that can solve reinforcement learning problems with embarrassing efficiency....
Reading
13 个视频 (总计 84 分钟), 7 个阅读材料, 3 个测验
Video13 个视频
Reinforcement learning vs all3分钟
Multi-armed bandit4分钟
Decision process & applications6分钟
Markov Decision Process5分钟
Crossentropy method9分钟
Approximate crossentropy method5分钟
More on approximate crossentropy method6分钟
Evolution strategies: core idea6分钟
Evolution strategies: math problems5分钟
Evolution strategies: log-derivative trick8分钟
Evolution strategies: duct tape6分钟
Blackbox optimization: drawbacks4分钟
Reading7 个阅读材料
What you're getting into1分钟
Setting up course environment10分钟
Note: this course vs github course1分钟
Course teaser placeholder10分钟
Primers1分钟
About honors track1分钟
Extras10分钟
2
完成时间(小时)
完成时间为 3 小时

At the heart of RL: Dynamic Programming

This week we'll consider the reinforcement learning formalisms in a more rigorous, mathematical way. You'll learn how to effectively compute the return your agent gets for a particular action - and how to pick best actions based on that return....
Reading
5 个视频 (总计 54 分钟), 2 个阅读材料, 4 个测验
Video5 个视频
State and Action Value Functions13分钟
Measuring Policy Optimality6分钟
Policy: evaluation & improvement10分钟
Policy and value iteration8分钟
Reading2 个阅读材料
Advanced Reward Design10分钟
Discrete Stochastic Dynamic Programming10分钟
Quiz3 个练习
Reward design8分钟
Optimality in RL10分钟
Policy Iteration14分钟
3
完成时间(小时)
完成时间为 5 小时

Model-free methods

This week we'll find out how to apply last week's ideas to the real world problems: ones where you don't have a perfect model of your environment....
Reading
6 个视频 (总计 47 分钟), 1 个阅读材料, 4 个测验
Video6 个视频
Monte-Carlo & Temporal Difference; Q-learning8分钟
Exploration vs Exploitation8分钟
Footnote: Monte-Carlo vs Temporal Difference2分钟
Accounting for exploration. Expected Value SARSA.11分钟
On-policy vs off-policy; Experience replay7分钟
Reading1 个阅读材料
Extras10分钟
Quiz1 个练习
Model-free reinforcement learning10分钟
4
完成时间(小时)
完成时间为 5 小时

Approximate Value Based Methods

This week we'll learn to scale things even farther up by training agents based on neural networks....
Reading
9 个视频 (总计 104 分钟), 3 个阅读材料, 5 个测验
Video9 个视频
Loss functions in value based RL11分钟
Difficulties with Approximate Methods15分钟
DQN – bird's eye view9分钟
DQN – the internals9分钟
DQN: statistical issues6分钟
Double Q-learning6分钟
More DQN tricks10分钟
Partial observability17分钟
Reading3 个阅读材料
TD vs MC10分钟
Extras10分钟
DQN follow-ups10分钟
Quiz3 个练习
MC & TD8分钟
SARSA and QLeaning8分钟
DQN12分钟
4.3
33 个审阅Chevron Right

热门审阅

创建者 TCMay 17th 2018

Great course. Best course so far on reinforcement learning.

讲师

Avatar

Pavel Shvechikov

Researcher at HSE and Sberbank AI Lab
HSE Faculty of Computer Science
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Alexander Panin

Lecturer
HSE Faculty of Computer Science

关于 National Research University Higher School of Economics

National Research University - Higher School of Economics (HSE) is one of the top research universities in Russia. Established in 1992 to promote new research and teaching in economics and related disciplines, it now offers programs at all levels of university education across an extraordinary range of fields of study including business, sociology, cultural studies, philosophy, political science, international relations, law, Asian studies, media and communications, IT, mathematics, engineering, and more. Learn more on www.hse.ru...

关于 Advanced Machine Learning 专项课程

This specialization gives an introduction to deep learning, reinforcement learning, natural language understanding, computer vision and Bayesian methods. Top Kaggle machine learning practitioners and CERN scientists will share their experience of solving real-world problems and help you to fill the gaps between theory and practice. Upon completion of 7 courses you will be able to apply modern machine learning methods in enterprise and understand the caveats of real-world data and settings....
Advanced Machine Learning

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