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.
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
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.
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-)
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