아마도

2017년 12월 18일 월요일

기타

difference between mass and gravity and drag
physic materials
add force, add torque
changing gravity
physics in slow-mo

void OnMouoseDown()
{
rigidbody.AddForce(-transform.forward * 500, ForceMode.Acceleration);
rigidbody.useGravity = true;
}

마우스 click 했을때 useGravity를 z축 방향으로 활성화
MouseDown Function 은 한번만

Feather has more drag

https://blog.naver.com/atelierjpro

https://www.slideshare.net/carpedm20/reinforcement-learning-an-introduction-64037079

2017년 12월 1일 금요일

1.DAgger

1.DAgger

https://github.com/zsdonghao/Imitation-Learning-Dagger-Torcs

2.etcher
https://docs.emlid.com/navio2/common/ardupilot/configuring-raspberry-pi/

3. change wifi id and password

4.RaspBerry Pi connect
https://www.youtube.com/watch?v=JjPsW-7FUng

5.sudo apt-get install smaba

6. navio 설정
https://docs.emlid.com/navio2/common/ardupilot/installation-and-running/

1.drone-rl_opencv_input

1. opencv 설치관련
https://www.scivision.co/install-opencv-python-windows/

2.GTA tutorial
https://www.youtube.com/watch?v=F4y4YOpUcTQ&list=PLQVvvaa0QuDeETZEOy4VdocT7TOjfSA8a&index=9

3.video capture
https://www.youtube.com/watch?v=xFGMfK3U9cI&list=PLVSe-_Zd7OCoL9Zeh9aSWZpSraqjVCuW7&index=1

4.기타
https://github.com/sentdex/pygta5
https://github.com/Unity-Technologies/ml-agents

5.Unity video capture
https://docs.unity3d.com/Manual/windowsholographic-videocapture.html

6.Unity Neural Network Tutorial
https://www.youtube.com/watch?v=Yq0SfuiOVYE
https://drive.google.com/drive/folders/0B26krjpa9ytcYWlHU0MweUt5Z2c
https://zip-extractor.appspot.com/?state=%7B%22ids%22:%5B%220B26krjpa9ytcOFBISWlIbmsxNmc%22%5D,%22action%22:%22open%22,%22userId%22:%22114481123904408930682%22%7D

wheel file installation

pip install some-package.whl

7.atari install

http://ishuca.tistory.com/390

2017년 11월 20일 월요일

project4_1 Prime Number

Q&A

1.https://www.blogger.com/blogger.g?blogID=5637323458874247490#editor/target=post;postID=6306941975301676117

seq_length = 11
data_dim = 2
hidden_dim = 200
output_dim = 1
learning_rate = 0.001
iterations = 1000

seq_length = 11
data_dim = 2
hidden_dim = 200
output_dim = 1
learning_rate = 0.001
iterations = 2000

seq_length = 11
data_dim = 2
hidden_dim = 200
output_dim = 1
learning_rate = 0.001
iterations = 10000

seq_length = 11
data_dim = 2
hidden_dim = 200
output_dim = 1
learning_rate = 0.001
iterations = 100000