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Joon's Lectures

2011년 2월 23일 수요일. We calculate the heuristic as the sum of Manhattan distances between boxes and the corresponding goals. We determine the corresponding goals of boxes as their position in a goal state. Recall that we expand all the nodes. An important property to note here is that we do not explicitly match the boxes with the goals. Mathematically, the heuristic of a state is calculated. 2011년 2월 22일 화요일. Improving the Optimality of RRT: RRT*. Is a vertex, and E. Is an edge. A graph, G. And an edge set E.

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2011년 2월 23일 수요일. We calculate the heuristic as the sum of Manhattan distances between boxes and the corresponding goals. We determine the corresponding goals of boxes as their position in a goal state. Recall that we expand all the nodes. An important property to note here is that we do not explicitly match the boxes with the goals. Mathematically, the heuristic of a state is calculated. 2011년 2월 22일 화요일. Improving the Optimality of RRT: RRT*. Is a vertex, and E. Is an edge. A graph, G. And an edge set E.

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1 joon's lectures

2 sokoban planner

3 state representation

4 method of planning

5 heuristics

6 execution file

7 whitebear

8 댓글 없음

9 1 introduction

10 2 related work

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Joon's Lectures | joonlecture.blogspot.com Reviews

https://joonlecture.blogspot.com

2011년 2월 23일 수요일. We calculate the heuristic as the sum of Manhattan distances between boxes and the corresponding goals. We determine the corresponding goals of boxes as their position in a goal state. Recall that we expand all the nodes. An important property to note here is that we do not explicitly match the boxes with the goals. Mathematically, the heuristic of a state is calculated. 2011년 2월 22일 화요일. Improving the Optimality of RRT: RRT*. Is a vertex, and E. Is an edge. A graph, G. And an edge set E.

INTERNAL PAGES

joonlecture.blogspot.com

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Joon's Lectures

http://joonlecture.blogspot.com/2010/07/machine-learning-algorithms-q-learning.html

2010년 7월 12일 월요일. 피드 구독하기: 댓글 (Atom). Machine Learning AlgorithmsQ-learningPath Planning.

2

Joon's Lectures: Sokoban Planner

http://joonlecture.blogspot.com/2011/02/sokoban-planner.html

2011년 2월 23일 수요일. We calculate the heuristic as the sum of Manhattan distances between boxes and the corresponding goals. We determine the corresponding goals of boxes as their position in a goal state. Recall that we expand all the nodes. An important property to note here is that we do not explicitly match the boxes with the goals. Mathematically, the heuristic of a state is calculated. 피드 구독하기: 댓글 (Atom). Improving the Optimality of RRT: RRT*.

3

Joon's Lectures: 4월 2010

http://joonlecture.blogspot.com/2010_04_01_archive.html

2010년 4월 14일 수요일. What is Q-learning and How to implement a resuable Q-learning? You may have a difficulty in making your software to be adaptable on interaction with human. The main reason is that in general, it is very hard to expect people’s responses in previous at various situations. How to solve the problem? Let's take a look at the algorithm of Q-learning, Aforementioned, Q-learning is composed with actions, states, rewards, Q-values and parameters. Their definitions are:. 피드 구독하기: 글 (Atom).

4

Joon's Lectures: 2월 2011

http://joonlecture.blogspot.com/2011_02_01_archive.html

2011년 2월 23일 수요일. We calculate the heuristic as the sum of Manhattan distances between boxes and the corresponding goals. We determine the corresponding goals of boxes as their position in a goal state. Recall that we expand all the nodes. An important property to note here is that we do not explicitly match the boxes with the goals. Mathematically, the heuristic of a state is calculated. 2011년 2월 22일 화요일. Improving the Optimality of RRT: RRT*. Is a vertex, and E. Is an edge. A graph, G. And an edge set E.

5

Joon's Lectures: Realistic Looking and Optimal A* Path Planning: Theta*

http://joonlecture.blogspot.com/2010/11/blog-post.html

2010년 11월 19일 금요일. Realistic Looking and Optimal A* Path Planning: Theta*. S is the set of all grid vertices, s. 8712; S is the start vertex of the search, and s. 8712; S is the goal vertex of the search. succ(s) ⊆ S is the set of neighbors of s ∈ S that have line-of-sight to s. c(s, s′) is the straight-line distance between s and s′ (both not necessarily vertices), and Lineofsight(s, s′) is true if and only if they have line-of-sight. Algorithm 1. Pseudo Code for Calculating Cost in Theta*. Figure 3&#46...

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Hypervelocity's World: January 2009

http://whitebearworld.blogspot.com/2009_01_01_archive.html

Wednesday, January 28, 2009. Projects have been done in Samsung. 2004] SHADOW Project - Graphics Library for Digital TV. 2005 2006] DSP Project - Digital TV Software Platform. 2007] MultiCube Project - Asynchronous File System for Flash Memory. 2008 ] Robo-Ray Project - Software Platform for Humanoid Robots. 2009 ] TAME Project - An Affective Component for Robots and CE Devices. Tuesday, January 27, 2009. Machine Learning and Algrorithm. This program is a back-propagation training progam. You can add...

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JoonKu's Personal Website.

Machine Learning에 관심이 많습니다. 초보적인 수준의 딥러닝 입문 튜토리얼. 와 Node.js 머신러닝 라이브러리. Your time is limited, so don't waste it living someone else's life. Seoul Science High School. Seoul National University (CSE). 딥러닝 입문 튜토리얼 (Deep Learning Tutorial for Beginners). Libraries / Open Source Activities. Deep learning library for Node.js. Machine learning library for Node.js. Windows NT Keyboard Control Library for Node.js. Windows NT Mouse Control Library for Node.js. 자유로운 강의 공유 사이트 개발. Comments powered by Disqus.

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joonlecture.blogspot.com

Joon's Lectures

2011년 2월 23일 수요일. We calculate the heuristic as the sum of Manhattan distances between boxes and the corresponding goals. We determine the corresponding goals of boxes as their position in a goal state. Recall that we expand all the nodes. An important property to note here is that we do not explicitly match the boxes with the goals. Mathematically, the heuristic of a state is calculated. 2011년 2월 22일 화요일. Improving the Optimality of RRT: RRT*. Is a vertex, and E. Is an edge. A graph, G. And an edge set E.

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