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Deep Learning & Machine Learning

Reinforcement Learning Basics Theory

This will be helpful to those who want to solidify their understanding of reinforcement learning theory and basic concepts, and those who want to learn how deep learning is applied to reinforcement learning.

(5.0) 24 reviews

2,655 learners

  • pangyolab8774
Reinforcement Learning(RL)

Reviews from Early Learners

What you will gain after the course

  • Reading reinforcement learning papers

Basic theory of reinforcement learning

Policy, reward, MDP, Monte-Carlo, temporal difference... These are concepts commonly encountered in reinforcement learning-related papers and projects. However, there aren't many lecture materials that thoroughly explain the precise definitions of each term, starting from the very basics. Jumping into a paper or project without a solid understanding of these concepts will leave you stranded, lost, like a ship without a rudder.

I believe the best resource for explaining the fundamentals, combining rich explanations with intuitive understanding, is DeepMind's Professor D. Silver's YouTube lecture. However, the lecture is conducted in English and can be somewhat challenging for beginners. Therefore, this lecture aims to re-explain the same content in Korean, making it easier to understand. Just as D. Silver's lecture consists of 10 lectures, ours will also consist of 10 lectures.

Helpful people

  • Those who want to solidify their understanding of reinforcement learning theory and basic concepts.
  • Anyone who wants to learn how deep learning is applied to reinforcement learning

AlphaGo paper review

If you're curious about what you can do with reinforcement learning, please first watch our Pangyo Lab's AlphaGo paper review video.
AlphaGo paper review: https://www.youtube.com/watch?v=SRVx2DFu_tY&list=PLpRS2w0xWHTfnWmr95LtIu4v4HbVxqTlM
AlphaGo Zero Paper Review: https://youtu.be/CgOGKChwWrw

What is reinforcement learning?

Reinforcement Learning, one of the fields of machine learning
 Machine learning can be broadly divided into supervised learning, unsupervised learning, and reinforcement learning. It's a method for recognizing the current state and selecting the action or sequence of actions that maximizes reward among available actions.

Introduction of knowledge sharers

No Seung-eun
Seoul National University - Computer Engineering and Economics (2010-2015)
Seoul National University Graduate School of Convergence Science and Technology - Research on Hyperparameter Optimization in Deep Learning (2015-2017)
NCsoft AI Research - Artificial Intelligence Researcher, Reinforcement Learning Team (2017-)

Jeon Min-young
Seoul National University - Computer Science and Visual Design (2011-2017)
Gameberry - Developer (2014)
Ringle - Developer (2015)
Madup - Developer (2016-2017)
Naver - Papago Team Front-end Development (2018-)

Recommended for
these people

Who is this course right for?

  • For those new to reinforcement learning

Need to know before starting?

  • differential

Hello
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2,655

Learners

24

Reviews

5.0

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1

Course

Curriculum

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10 lectures ∙ (13hr 2min)

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Reviews

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24 reviews

5.0

24 reviews

  • blaire83님의 프로필 이미지
    blaire83

    Reviews 9

    ∙

    Average Rating 5.0

    5

    100% enrolled

    Just listening to the first lecture was already great!!!!!!!!!!!!!!!!!!! If you want to properly understand reinforcement learning from the basics, if you want to properly apply it to papers or research, this seems like such a great lecture. I will definitely complete the entire course. Thank you.

    • jjhgwx님의 프로필 이미지
      jjhgwx

      Reviews 609

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      Average Rating 4.9

      5

      30% enrolled

      Thank you for the great lecture!

      • jh41gong5625님의 프로필 이미지
        jh41gong5625

        Reviews 2

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        Average Rating 5.0

        5

        60% enrolled

        • kukaeden님의 프로필 이미지
          kukaeden

          Reviews 486

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          Average Rating 5.0

          5

          40% enrolled

          I learned a lot about reinforcement learning. Thank you for the great lecture~^^

          • devkuka님의 프로필 이미지
            devkuka

            Reviews 286

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            Average Rating 5.0

            5

            30% enrolled

            I had a lot of questions about reinforcement learning, but I learned a lot! Thank you for the great lecture~^^

            Free

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