artificial intelligence - unite seouluniteseoul.com/2019/pdf/d2t5s4.pdf · 2020. 2. 6. · obstacle...

Artificial Intelligence & Deep Learning

Artificial Intelligence

3

— Classification

— Regression

— Natural Language Processing

— Object Detection

— Generative Model

— Reinforcement Learning

4

Deep Learning

Processing unit of Brain

Deep Learning

5

Processing unit of Neural Network

Input Output

Deep Learning

6

~ 2010

Deep Learning

7

Algorithms GPU Big Data

Deep Learning

8

2010~

Classification

9

Regression

10

Natural Language Processing

11

— OpenAI’s GPT-2

Object Detection

12

Generative Model

13

Generative Model

14

Generative Model

15

Reinforcement Learning

16

Deep Learning in Games

Generative Adversarial Networks (GAN)

Deep Learning in Games

18

Reinforcement Learning (RL)

VS

GAN

19

Real?

Fake?

Real

Fake

GAN

20

Real

Fake

Real?

Fake?

Generator

Discriminator

GAN

21

Training

GAN

22

Generator

Game Level Generation Using GAN

23

Game Level Generation Using GAN

24

Game Level Generation Using GAN

25

Design Using GAN

26

Generator

Condition

Design Using GAN

27

Generator

Sword

Reinforcement Learning

28

Reward

+ Reward - Reward

Reinforcement Learning

29

Reward

+ Reward - Reward

Reinforcement Learning

30

Agent Environment

State, Reward

Action

Reinforcement Learning

31

GridWorld Starcraft2

Reinforcement Learning

32

Reinforcement Learning

33

Reinforcement Learning

34

Reinforcement Learning

35

Reinforcement Learning

36

Reinforcement Learning

37

https://github.com/reinforcement-learning-kr/alpha_omok

Reinforcement Learning

38

Reinforcement Learning

39

Reinforcement Learning

40

Reinforcement Learning

41

— Multi-agents RL

— Meta RL

— Exploration

— Curiosity

— Noise in parameter

— Model-based RL

— Sim2Real

Reinforcement Learning

42

https://www.facebook.com/groups/ReinforcementLearningKR/

AI in Unity

AI in Unity

44

Challenges Machine Learning Agents

ML-agents Challenge

45

ML-agents Challenge

46

ML-agents Challenge

47

Obstacle Tower Challenge

48

Obstacle Tower Challenge

49

— Montezuma’s Revenge

— Challenges— Sparse reward

— Hard exploration

— Requires planning

— Multi Task

Hard to Solve!!

Obstacle Tower Challenge

50

— Montezuma’s Revenge is Solved!

— Demonstration— Aytar et al. 2018

— Curiosity

– Pathak er al. 2017

– Burda et al. 2018

— Go-Explore

– Ecoffet et al. 2018

Go-Explore: a New Approach for Hard-Exploration Problems (Ecoffet et al.)

Obstacle Tower Challenge

51

Obstacle Tower Challenge

52

Obstacle Tower Challenge

53

— 3D Visual Observation

— Complex floor layout

— Generalization

– Floor, Room, Wall

– Every 10 floors

— Multi Task

– Key

– Sokoban

– Pit

Obstacle Tower Challenge

54

— ModuLabs CTRL Team— Kyushik Min

— Jay Jung

— Suhyuk Park

— Hyojeong Jeon

— Round 1 is finished! ☺

— Curiosity based algorithm

— Average 6~7th floor

Obstacle Tower Challenge

55

Unity ML-agents

56

Unity ML-agents

57

— Reinforcement Learning

Agent Environment

State, Reward

Action

Unity ML-agents

58

— Deep Reinforcement Learning

Agent Environment

?

State, Reward

Action

Unity ML-agents

59

— Deep Reinforcement Learning

Agent Environment

?

State, Reward

Action

Unity ML-agents

60

— Deep Reinforcement Learning

Agent Environment

State, Reward

Action

Unity ML-agents

61

Academy

- Managing brains

- Configuration setting

Brain

- Observation setting

- Action setting

Agent

- Script for Agent

- Control Setting

- Reward, done setting

Unity ML-agents

62

Unity ML-agents

63

VS: Single Agent

: Multi-Agent

: Adversarial Agents

: Imitation Learning

Unity ML-agents

64

: Training

: Heuristic

: PlayerBrain

: External

: Internal

Unity ML-agents

65

Agent1

Brain1 (Heuristic)

Agent2

Brain2 (Internal)

Agent4

Brain3 (External)

Agent3 Agent5

Academy

Deep Learning

Unity ML-agents

66

Reinforcement Learning Unity

Unity ML-agents Tutorial

67

RL Korea Unity ML-agents Tutorial Team

https://github.com/reinforcement-learning-kr/Unity_ML_Agents

Sokoban

- Discrete Action

- Deep Q-Network (DQN)

Unity ML-agents Tutorial

68

Drone

- Continuous Action

- Deep Deterministic Poli

cy Gradient (DDPG)

Pong

- Adversarial Environment

- Discrete Action

- DQN

Sokoban (Curriculum)

- Curriculum Learning

- Discrete Action

- DQN

Unity ML-agents Tutorial

69

Dodge

- Imitation Learning

- Discrete Action

- Behavioral Cloning

TwoLeg Walker

- Multi-agents

- Continuous Action

- MADDPG

Unity ML-agents Tutorial

70

Unity ML-agents Tutorial

71

Unity ML-agents Tutorial

72

Unity ML-agents Tutorial

73

Unity ML-agents Tutorial

74

Unity ML-agents Tutorial

75

Unity ML-agents Tutorial

76

Thank you

77

https://www.facebook.com/groups/ReinforcementLearningKR/