We will learn about Q-learning and Deep Q-learning, and have time to implement reinforcement learning in R. We will cover the entire reinforcement learning content, from Deep Q-network to Self-imitation learning and Random Network Distillation.
Reinforcement learning theory
From Q-learning to Deep Reinforcement Learning
Several reinforcement learning techniques for exploration
🙆🏻♀ Beyond Q-learning and Deep Q-learning to RND🙆🏻♂
The reinforcement learning boom started with AlphaGo. Did you know that reinforcement learning was an algorithm that existed long before AlphaGo?
Reinforcement learning is generally known as a field with a high barrier to entry. While the emergence of AlphaGo sparked interest, the complex nature of the subject makes it challenging to learn. For those who have been eager to learn reinforcement learning but have been hesitant to even begin, this course summarizes the key points. From Q-learning to DQN, and beyond DQN, this course explores the sparse reward problem, a key challenge in reinforcement learning, and various ideas for solving it. This course will provide a comprehensive overview of reinforcement learning in a short period of time.
We will explain step-by-step, using examples, what reinforcement learning is, what elements it contains, and how learning progresses.
Explaining it in words alone isn't enough. Let's try solving Q-learning problems ourselves to truly grasp the concepts of reinforcement learning.
This book summarizes the core concepts of Deep Reinforcement Learning, from Deep Q-network (DQN) to various DQN variants including PerDQN, actorcritic, and Self-Imitation learning.
We will discuss the sparse reward problem, which is a major problem in reinforcement learning, and discuss various techniques to solve it.
We mainly talk about 'curiosity' or 'prediction error' and introduce several algorithms that utilize them.
(SIL, Random Network Distillation, etc.)
Unless you implement it yourself with code, you only know half of it, right? Let's write reinforcement learning algorithms for the most important models in R and examine the results together.
And let's also see if RND for Exploration really works.
Q. Do you have any player knowledge?
A. It would be good to have a basic understanding of machine learning and NN.
Q. Don't you do any practical training with Python?
A. Currently, I have uploaded the lecture by implementing the practical code in R, and I plan to upload the practical code in Python in the future. (I will upload it in addition to this lecture, rather than opening it as another lecture.)
Who is this course right for?
For those who want to learn reinforcement learning easily
Anyone who wants to learn the entire reinforcement learning in a short period of time
Need to know before starting?
Intermediate R programming skills
Basic understanding of neural networks
Basic knowledge of machine learning
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504
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Answers
4.4
Rating
20
Courses
학부에서는 통계학을 전공하고 산업공학(인공지능) 박사를 받고 여전히 공부중인 백수입니다.
수상
ㆍ 제6회 빅콘테스트 게임유저이탈 알고리즘 개발 / 엔씨소프트상(2018)
ㆍ 제5회 빅콘테스트 대출 연체자 예측 알고리즘개발 / 한국정보통신진흥협회장상(2017)
ㆍ 2016 날씨 빅데이터 콘테스트/ 기상산업 진흥원장상(2016)
ㆍ 제4회 빅콘테스트 보험사기 예측 알고리즘 개발 / 본선진출(2016)
ㆍ 제3회 빅콘테스트 야구 경기 예측 알고리즘 개발 / 미래창조과학부 장관상(2015)
* blog : https://bluediary8.tistory.com
주로 연구하는 분야는 데이터 사이언스, 강화학습, 딥러닝 입니다.
크롤링과 텍스트마이닝은 현재는 취미로 하고있습니다 :)
크롤링을 이용해서 인기있는 커뮤니티 글만 수집해서 보여주는 마롱이라는 앱을 개발하였고
전국의 맛집리스트와 블로그를 수집해서 맛집 추천 앱도 만들었었죠 :) (시원하게 말아먹..)
지금은 인공지능을 연구하는 박사과정생입니다.
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20 lectures ∙ (4hr 31min)
Course Materials:
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Tôi đã cố gắng xem một số bài giảng về học tăng cường, nhưng cá nhân tôi nghĩ rằng người này giải thích bức tranh tổng thể về học tăng cường rất hay và theo một cách thú vị. Mình mới xem đến phần 2 thôi nhưng nó khiến mình tò mò. Tuy nhiên, vì tôi có quyền truy cập hạn chế vào R nên tôi khó hiểu mã, vì vậy tôi thực sự hy vọng rằng mã Python sẽ sớm ra mắt.
$42.90
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