View source for 데블스캠프2017/강화학습으로컴퓨터에게고전게임플레이시키기

__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

== 실습 ==
* [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.
** 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 ==