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Reinforcement Learning All-in-One: Fundamentals, Latest Algorithms, and Practical Applications

AI that thinks and adapts? Now you can build it yourself. Master the core of reinforcement learning with intuitive explanations and code, not complex theories, and grow into a 'practical developer' through finance and industry projects.

(5.0) 6 reviews

182 learners

  • multicoreit
강화학습
인공지능
AI
ai활용
에이전트
Python
Artificial Neural Network
Reinforcement Learning(RL)
Fine-Tuning
optimization-problem

What you will gain after the course

  • Reinforcement Learning Basic Theory (Mathematics, Statistics, MDP)

  • Artificial Neural Network Concepts (Novel Regression, Classification Analysis, Artificial Neural Network)

  • Reinforcement Learning Algorithms (DQN, REINFORCE, A2C, PPO)

  • Algorithm Tuning (Optuna) and Framework (Stable Baselines3)

  • Practical Examples (Asset Allocation Strategy, Branch Rotation Modeling)

Systems that Judge and Adapt on Their Own: Growing into a Practical Developer

For those who have hesitated to study reinforcement learning due to mathematical theories and complex code, the "Learning Reinforcement Learning with Code Like a Developer" course has finally arrived. Through reinforcement learning, you'll develop practical development skills to create intelligent systems that can make decisions and adapt on their own in unpredictable situations.

  • We've added even more kind and intuitive explanations.

  • We have added the latest practical tools (Stable Baselines3) and techniques (Optuna).

  • I implemented rich practical example projects (asset allocation strategy, branch rotation system).

I recommend this for people like this

[[STRONG_1]]강화학습 이론의 수학적, 이론적 장벽 앞에서 주저했던 [[/STRONG_1]][[SPAN_2]][[STRONG_3]]프로그래머 [[/STRONG_3]][[/SPAN_2]][[SPAN_4]][[STRONG_5]]또는[[/STRONG_5]][[/SPAN_4]][[SPAN_6]][[STRONG_7]] 개발자[[/STRONG_7]][[/SPAN_6]]

Practitioners who want to solve complex problems in actual financial markets or industrial settings using reinforcement learning or individual investors.

Beyond simple task automation, programmers who want to create intelligent systems that judge and adapt on their own according to situations

After taking the course

  • You can overcome mathematical barriers and connect the core concepts and code of reinforcement learning.

  • You can efficiently build and optimize reinforcement learning models using cutting-edge practical tools like Stable-Baselines3 and Optuna.

  • You can gain practical experience in modeling and solving complex real-world problems with reinforcement learning, such as financial asset allocation and workforce deployment in industrial settings.

  • You can grow into a practical reinforcement learning developer capable of designing and implementing intelligent systems that make autonomous decisions and adapt according to different situations.

Features of this course

2025-06-10 13;25;46

A practical, programmer-friendly approach that overcomes mathematical barriers

Instead of complex formulas, learn core theories like MDP and value functions through intuitive code examples. Through Python-based step-by-step practice, you'll directly grasp reinforcement learning principles and gain confidence to apply them in practice.

Cultivating real-world problem-solving skills through the use of cutting-edge tools and rich hands-on projects

Latest algorithms (PPO, A2C) and Stable-Baselines3, Optuna optimization techniques are covered. Through real projects such as financial asset allocation and workforce deployment, you will develop the capability to build intelligent systems that can learn even without data.

You'll learn this kind of content

Reinforcement Learning Fundamentals and Embodying Probabilistic Thinking

From probability/statistics concepts to agent-environment interaction mechanisms, this forms the foundation of reinforcement learning. You'll learn Markov Decision Processes (MDP), value functions, Q-functions, and build problem-solving fundamentals with early algorithms such as dynamic programming, Monte Carlo, and temporal difference learning.

Artificial Neural Networks and Function Approximation Applications

Learn the fundamental principles of artificial neural networks, which are the core of modern reinforcement learning. You'll learn how to solve complex state/action space problems through function approximation, and understand how neural networks learn through loss functions and gradient descent.

Deep Learning of Latest Reinforcement Learning Algorithms

We cover major algorithms such as DQN, REINFORCE, A2C, PPO in detail. Using Stable-Baselines3 (SB3), we build and train models while learning core techniques including experience replay, policy gradients, Actor-Critic architecture, clipping.

Artificial Neural Network Tuning and Hyperparameter Automatic Optimization

You'll learn practical tuning techniques including input data preprocessing, activation functions, weight initialization, optimization algorithms, and network architecture design. Maximize model performance by finding optimal hyperparameters through Bayesian optimization using automated optimization tools like Optuna.

Real-world project experience in finance and industrial sectors

Through projects on asset allocation strategy optimization based on real financial data and corporate branch rotation workforce deployment modeling, you will develop the ability to apply reinforcement learning in real-world scenarios. You will grow into a practical developer by directly experiencing the entire process from problem definition to environment setup, agent design, model training, tuning, and result analysis.

The person who created this course

  • Author of the book "Reinforcement Learning for Programmers" and,

  • The best reinforcement learning lectures in Korea are being conducted on Inflearn.

  • Beyond complex theories, I share reinforcement learning know-how that can be applied to solving real-world problems with you.

  • Corporate and Individual Lecture Inquiries: multicore.it@gmail.com

Do you have any questions?

Q. What is reinforcement learning and where is it used?

Reinforcement learning is an autonomous learning method that finds optimal strategies on its own. Beyond game AI, it is utilized to solve unpredictable real-world problems such as financial investment strategies (stock and cryptocurrency trading) and corporate workforce allocation optimization. It learns through trial and error by interacting with the environment and maximizes long-term performance.

Q. Can I take this course even if I don't know math or programming?

High school level math concepts (basic arithmetic, matrices, etc.) are sufficient. We focus on intuitive explanations rather than complex formulas, and deep learning frameworks handle most of the calculations for you. Minimal programming knowledge to read and follow Python code is needed, but don't worry - we provide detailed guidance from Python installation to development environment setup.

Q. What does 'intuitive concept explanation' mean? Are there no formulas?

Focus on intuitive understanding rather than formulas. Core concepts like Markov Decision Process (MDP) and reward functions are explained through rich illustrations, diagrams, and analogies (dice, drawing balls, etc.) and various examples. While formulas aren't completely absent, the focus is on how to connect the conceptual meaning to Python code rather than complex proofs.

Q. Do you teach development environment setup from the very basics?

Yes, we provide detailed guidance from the very basics. We explain step-by-step from Python installation to Jupyter Notebook, Stable-Baselines3, Optuna, TensorFlow, and other essential library installations. Even if you have no experience with development environment setup, you can configure everything without any issues by following the course guidance.

Q. I'm new to Stable-Baselines3(SB3) and Optuna, will that be okay?

Yes, we explain in detail so that even beginners can easily learn. Stable-Baselines3 helps implement reinforcement learning without complex deep learning knowledge through its intuitive usage. Optuna is a Bayesian optimization tool for hyperparameter tuning, and we cover basic concepts, usage methods, and practical applications in financial asset allocation examples with specific code.

Q. Can I work on real-world projects hands-on? What's the difficulty level?

네, [[STRONG_1]]실제 금융 데이터 기반 자산 배분[[/STRONG_1]]과 [[STRONG_2]]기업 지점 순환 근무 모델링[[/STRONG_2]] 프로젝트를 [[STRONG_3]]직접 코딩하며 경험[[/STRONG_3]]합니다. 이론만 배우는 것이 아니라 강화학습 전략이 실제 수익률이나 문제 해결에 어떤 영향을 주는지 직접 확인합니다. '실전 가이드'인 만큼, [[STRONG_4]]단계적으로 따라갈 수 있도록 구성[[/STRONG_4]]되어 처음 접하는 분들도 프로젝트를 수행할 수 있습니다.

Q. What skills will I gain from taking this course?

Beyond understanding reinforcement learning theory, you will become a practical developer who 'designs agents, creates environments, tunes strategies, and evaluates returns'. You will be able to design reinforcement learning-based financial investment strategies and implement AI models that solve optimization problems like workforce allocation. Ultimately, you will develop the capability to create intelligent systems that make decisions and adapt on their own.

Pre-enrollment Reference Information

Practice Environment

  • Operating System and Version (OS): Windows 10 or higher

  • Tools used: Python 3.10.3, Jupyter notebook

  • PC Specifications: PC specifications capable of running MS Word

Learning Materials

Recommended for
these people

Who is this course right for?

  • Programmer who hesitated before Reinforcement Learning's mathematical barrier

  • Practitioners and individual investors who want to solve complex real-world problems like financial investment, human resource allocation, etc.

  • A developer aspiring to create self-judging intelligent systems beyond simple automation.

Need to know before starting?

  • Python Programming Basic Knowledge

Hello
This is

841

Learners

48

Reviews

112

Answers

4.7

Rating

4

Courses

멀티코어는 프로그래머이자 인공지능 전문가입니다. 프로그래머로서 다양한 분야에서 활동했으며 현재는 기업에서 데이터분석과 강화학습을 활용한 비즈니스 환경 개선 업무를 담당하고 있습니다. 인공지능이 학위를 받은 소수의 전문가만을 위한 영역이 아니라 프로그래머도 충분히 도전할 수 있음을 후배들에게 보여주기 위해 부단히 노력하고 있습니다. "프로그래머를 위한 강화학습"을 집필했습니다.

 

  • 집필 및 자격사항

  1. 개발자 답게 코드로 익히는 강화학습 (2025) / 프리렉

  2. 딥페이크 모델 분석을 통한 딥페이크 이미지 분류 개선에 대한 고찰 (2024) / 한국융합보안학회

  3. 비트코인 선물 자동매매시스템 집필 (2022) / 프리렉

  4. 프로그래머를 위한 강화학습 집필 (2021) / 프리렉

  5. 멀티플 DOM 트리를 활용한 브라우저 퍼징기법 연구(2017) / 연세대학교

     

  6. 정보시스템 수석감리원 자격증 취득(2015) / 정보시스템감리협회

     

  7. 컴퓨터시스템응용기술사 (2013) / 한국산업인력공단

  • 기업 및 개인 강의 문의 : multicore.it@gmail.com

Curriculum

All

40 lectures ∙ (8hr 0min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

6 reviews

5.0

6 reviews

  • nanamjk8391님의 프로필 이미지
    nanamjk8391

    Reviews 3

    Average Rating 5.0

    5

    11% enrolled

    I had taken Multicore-nim's Reinforcement Learning for Programmers, and when a new reinforcement learning course became available this time, I decided to take it again. Since RL is such a challenging field, I had difficulty understanding it because I couldn't find a course that suited me. Multicore-nim's course was a ray of light for me in that situation. Now, seeing RL frequently mentioned in related research papers and technologies, I think I made a really good decision to take the course back then. This course is very helpful to me because it explains practical, real-world examples well. I don't think there's another RL course in Korea as good as this one, so I highly recommend Multicore-nim's course.

    • multicoreit
      Instructor

      Thank you, Baguette-nim, for the good review. I will always show you my efforts.

  • 94sjh6973님의 프로필 이미지
    94sjh6973

    Reviews 2

    Average Rating 5.0

    Edited

    5

    8% enrolled

    Reinforcement learning isn't as hard as I thought. It was good that it was explained simply. It's good that even beginners can understand it. Thank you.

    • multicoreit
      Instructor

      Son Jeong-ho, thank you for the good course review. I will always show you that I am making an effort.

  • abcd123123님의 프로필 이미지
    abcd123123

    Reviews 325

    Average Rating 5.0

    5

    100% enrolled

    • multicoreit님의 프로필 이미지
      multicoreit

      Reviews 2

      Average Rating 5.0

      5

      100% enrolled

      • bertter8544님의 프로필 이미지
        bertter8544

        Reviews 1

        Average Rating 5.0

        5

        32% enrolled

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