Latest revision as of 00:34, 29 March 2026

machine learning

머신 러닝의 세가지 분류

Supervised learning
Unsupervised learning
Reinforcement learning

supervised learning

학습을 시킬 때 input으로 feature(입력값)와 label(원하는 결과값)을 함께 전달
Learning from difference between prediction and label
e.g. mnist, classification

unsupervised learning

input: feature만 입력, 보통 projection등으로 feature의 차원을 축소시킨다.
Cluster by distance between inputs
Human can't predict the outcome
e.g. clustering

reinforcement learning

일종의 unsupervised learning
input : environment, reward, output : action
Learn from try
- Model free: 게임의 규칙을 알려주지 않음
e.g. game play, stock trading

reinforcement learning

Q learning
Q learning + Neural Network
DQN : Deep Q Learning
- hidden layer를 늘리는게 다가 아니다!

Basic knowledge

MDP : Markov Decision Process
Bellman equation
Dynamic programming
Value, Polish
Value function, Polish function
Value iteration, Polish iteration

실습

gym: Reinforcement learning을 위한 고전 게임들을 python으로 포팅한 toolkit. 직접 구현한 것도 있고 atari는 포팅함. github에 코드가 공개되어 있다.
- 오늘 실습할 cartpole
필요한 라이브러리: numpy, gym, tensorflow 필요

  $ pip install gym
  $ pip install tensorflow

순서

일단 cartpole 실행을 해보자! - cartpole_init.py
random action(왼쪽, 오른쪽)을 하는 cartpole - cartpole_random.py
q-network(q-learning의 NN버전) - cartpole_qnetwork.py
DQN - cartpole_dqn.py
2015에 Deep Mind에서 발표한 DQN - cartpole_dqn2015.py

Reference

발표 슬라이드: slide
실습코드: github
DeepMind의 DQN 논문: Playing Atari with Deep Reinforcement Learning
Tensorflow tutorial: DQN

Furthermore

David Silver의 강의
- 강의노트
- 강의 영상
Gitbook: Fundamental of Reinforcement Learning. 한글로 되어 있다!
레퍼런스 모음: Machine Learning

@@ Line 1: / Line 1: @@
+__TOC__
 = machine learning =
+머신 러닝의 세가지 분류
+# Supervised learning
+# Unsupervised learning
+# Reinforcement learning
+== supervised learning ==
+* 학습을 시킬 때 input으로 feature(입력값)와 label(원하는 결과값)을 함께 전달
+* Learning from difference between prediction and label
+* e.g. mnist, classification
+== unsupervised learning ==
+* input: feature만 입력, 보통 projection등으로 feature의 차원을 축소시킨다.
+* Cluster by distance between inputs
+* Human can't predict the outcome
+* e.g. clustering
+== reinforcement learning ==
+* 일종의 unsupervised learning
+* input : environment, reward, output : action
+* Learn from try
+** Model free: 게임의 규칙을 알려주지 않음
+* e.g. game play, stock trading
+== reinforcement learning ==
+* Q learning
+* Q learning + Neural Network
+* DQN : Deep Q Learning
+** hidden layer를 늘리는게 다가 아니다!
+== Basic knowledge ==
+* MDP : Markov Decision Process
+* Bellman equation
+* Dynamic programming
+* Value, Polish
+* Value function, Polish function
+* Value iteration, Polish iteration
-* supervised learning
+== 실습 ==
-* unsupervised learning
+* [https://gym.openai.com gym]: Reinforcement learning을 위한 고전 게임들을 python으로 포팅한 toolkit. 직접 구현한 것도 있고 atari는 포팅함. [https://github.com/openai/gym github]에 코드가 공개되어 있다.
-* reinforcement learning
+** 오늘 실습할 [https://gym.openai.com/envs/CartPole-v0 cartpole]
+* 필요한 라이브러리: numpy, gym, tensorflow 필요
+   $ pip install gym
+   $ pip install tensorflow
+=== 순서 ===
+# 일단 cartpole 실행을 해보자! - [https://github.com/Rabierre/cartpole/blob/master/cartpole_init.py cartpole_init.py]
+# random action(왼쪽, 오른쪽)을 하는 cartpole - [https://github.com/Rabierre/cartpole/blob/master/cartpole_random.py cartpole_random.py]
+# q-network(q-learning의 NN버전) - [https://github.com/Rabierre/cartpole/blob/master/cartpole_qnetwork.py cartpole_qnetwork.py]
+# DQN - [https://github.com/Rabierre/cartpole/blob/master/cartpole_dqn.py cartpole_dqn.py]
+# 2015에 Deep Mind에서 발표한 DQN - [https://github.com/Rabierre/cartpole/blob/master/cartpole_dqn2015.py cartpole_dqn2015.py]
+== Reference ==
+* 발표 슬라이드: [https://slides.com/rabierre/playing_a_game_with_rl slide]
+* 실습코드: [https://github.com/Rabierre/cartpole github]
+* DeepMind의 DQN 논문: [https://arxiv.org/abs/1312.5602 Playing Atari with Deep Reinforcement Learning]
+* Tensorflow tutorial: [https://github.com/golbin/TensorFlow-Tutorials/tree/master/10%20-%20DQN DQN]
+== Furthermore ==
+* [https://en.wikipedia.org/wiki/David_Silver_(programmer) David Silver]의 강의
+** [http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html 강의노트]
+** [https://www.youtube.com/watch?v=2pWv7GOvuf0 강의 영상]
+* Gitbook: [https://www.gitbook.com/book/dnddnjs/rl/details Fundamental of Reinforcement Learning]. 한글로 되어 있다!
+* 레퍼런스 모음: [[Machine Learning]]
+== 후기 및 기타의견 ==