• Title/Summary/Keyword: 심층강화학습

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Map-Based Obstacle Avoidance Algorithm for Mobile Robot Using Deep Reinforcement Learning (심층 강화학습을 이용한 모바일 로봇의 맵 기반 장애물 회피 알고리즘)

  • Sunwoo, Yung-Min;Lee, Won-Chang
    • Journal of IKEEE
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    • v.25 no.2
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    • pp.337-343
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    • 2021
  • Deep reinforcement learning is an artificial intelligence algorithm that enables learners to select optimal behavior based on raw and, high-dimensional input data. A lot of research using this is being conducted to create an optimal movement path of a mobile robot in an environment in which obstacles exist. In this paper, we selected the Dueling Double DQN (D3QN) algorithm that uses the prioritized experience replay to create the moving path of mobile robot from the image of the complex surrounding environment. The virtual environment is implemented using Webots, a robot simulator, and through simulation, it is confirmed that the mobile robot grasped the position of the obstacle in real time and avoided it to reach the destination.

Study of Deep Reinforcement Learning-Based Agents for Controlled Flight into Terrain (CFIT) Autonomous Avoidance (CFIT 자율 회피를 위한 심층강화학습 기반 에이전트 연구)

  • Lee, Yong Won;Yoo, Jae Leame
    • Journal of the Korean Society for Aviation and Aeronautics
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    • v.30 no.2
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    • pp.34-43
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    • 2022
  • In Efforts to prevent CFIT accidents so far, have been emphasizing various education measures to minimize the occurrence of human errors, as well as enforcement measures. However, current engineering measures remain in a system (TAWS) that gives warnings before colliding with ground or obstacles, and even actual automatic avoidance maneuvers are not implemented, which has limitations that cannot prevent accidents caused by human error. Currently, various attempts are being made to apply machine learning-based artificial intelligence agent technologies to the aviation safety field. In this paper, we propose a deep reinforcement learning-based artificial intelligence agent that can recognize CFIT situations and control aircraft to avoid them in the simulation environment. It also describes the composition of the learning environment, process, and results, and finally the experimental results using the learned agent. In the future, if the results of this study are expanded to learn the horizontal and vertical terrain radar detection information and camera image information of radar in addition to the terrain database, it is expected that it will become an agent capable of performing more robust CFIT autonomous avoidance.

Application Trends of Deep Learning Artificial Intelligence in Autonomous Things (자율사물을 위한 심층학습 인공지능 기술 적용 동향)

  • Cho, J.M.
    • Electronics and Telecommunications Trends
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    • v.35 no.6
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    • pp.1-11
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    • 2020
  • Recently, autonomous things, which are pieces of equipment or devices that grasp the context of circumstances on their own and perform actions appropriate for the situation in the surrounding environment, are attracting much research interest. This is because autonomous things are expected to be able to interact with humans more naturally, supersede humans in many tasks, and further solve problems by themselves by collaborating with each other without human intervention. This prospect leans heavily on AI as deep learning has delivered astonishing breakthroughs recently and broadened its range of applications. This paper surveys application trends in deep learning-based AI techniques for autonomous things, especially autonomous driving vehicles, because they present a wide range of problems involving perception, decision, and actions that are very common in other autonomous things.

A Distributed Scheduling Algorithm based on Deep Reinforcement Learning for Device-to-Device communication networks (단말간 직접 통신 네트워크를 위한 심층 강화학습 기반 분산적 스케쥴링 알고리즘)

  • Jeong, Moo-Woong;Kim, Lyun Woo;Ban, Tae-Won
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.24 no.11
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    • pp.1500-1506
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    • 2020
  • In this paper, we study a scheduling problem based on reinforcement learning for overlay device-to-device (D2D) communication networks. Even though various technologies for D2D communication networks using Q-learning, which is one of reinforcement learning models, have been studied, Q-learning causes a tremendous complexity as the number of states and actions increases. In order to solve this problem, D2D communication technologies based on Deep Q Network (DQN) have been studied. In this paper, we thus design a DQN model by considering the characteristics of wireless communication systems, and propose a distributed scheduling scheme based on the DQN model that can reduce feedback and signaling overhead. The proposed model trains all parameters in a centralized manner, and transfers the final trained parameters to all mobiles. All mobiles individually determine their actions by using the transferred parameters. We analyze the performance of the proposed scheme by computer simulation and compare it with optimal scheme, opportunistic selection scheme and full transmission scheme.

Zero-shot Text Classification based on Reinforced Learning (강화학습 기반의 제로샷 텍스트 분류)

  • Zhang Songming;Inwhee Joe
    • Proceedings of the Korea Information Processing Society Conference
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    • 2023.11a
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    • pp.439-441
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    • 2023
  • 전통적인 텍스트 분류 방법은 상당량의 라벨링된 데이터와 미리 정의된 클래스가 필요해서 그 적용성과 확장성이 제한된다. 그래서 이런 한계를 극복하기 위해 제로샷 러닝(Zero-shot Learning)이 등장했다. 텍스트 분류 분야에서 제로샷 텍스트 분류는 모델이 대상 클래스의 샘플을 미리 접하지 않고도 인스턴스를 분류할 수 있도록 하는 중요한 주제이다. 이 문제를 해결하기 위해 정책 네트워크를 활용한 심층 강화 학습(DRL) 기반 접근법을 제안한다. 이러한 방법을 통해 모델이 새로운 의미 공간에 효과적으로 적응하면서, 다른 모델들과 비교하여 제로샷 텍스트 분류의 정확도를 향상시킬 수 있었다. XLM-R 과 비교하면 최대 15.9%의 정확도 향상이 나타났다.

Grasping a Target Object in Clutter with an Anthropomorphic Robot Hand via RGB-D Vision Intelligence, Target Path Planning and Deep Reinforcement Learning (RGB-D 환경인식 시각 지능, 목표 사물 경로 탐색 및 심층 강화학습에 기반한 사람형 로봇손의 목표 사물 파지)

  • Ryu, Ga Hyeon;Oh, Ji-Heon;Jeong, Jin Gyun;Jung, Hwanseok;Lee, Jin Hyuk;Lopez, Patricio Rivera;Kim, Tae-Seong
    • KIPS Transactions on Software and Data Engineering
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    • v.11 no.9
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    • pp.363-370
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    • 2022
  • Grasping a target object among clutter objects without collision requires machine intelligence. Machine intelligence includes environment recognition, target & obstacle recognition, collision-free path planning, and object grasping intelligence of robot hands. In this work, we implement such system in simulation and hardware to grasp a target object without collision. We use a RGB-D image sensor to recognize the environment and objects. Various path-finding algorithms been implemented and tested to find collision-free paths. Finally for an anthropomorphic robot hand, object grasping intelligence is learned through deep reinforcement learning. In our simulation environment, grasping a target out of five clutter objects, showed an average success rate of 78.8%and a collision rate of 34% without path planning. Whereas our system combined with path planning showed an average success rate of 94% and an average collision rate of 20%. In our hardware environment grasping a target out of three clutter objects showed an average success rate of 30% and a collision rate of 97% without path planning whereas our system combined with path planning showed an average success rate of 90% and an average collision rate of 23%. Our results show that grasping a target object in clutter is feasible with vision intelligence, path planning, and deep RL.

Prediction Technique of Energy Consumption based on Reinforcement Learning in Microgrids (마이크로그리드에서 강화학습 기반 에너지 사용량 예측 기법)

  • Sun, Young-Ghyu;Lee, Jiyoung;Kim, Soo-Hyun;Kim, Soohwan;Lee, Heung-Jae;Kim, Jin-Young
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.21 no.3
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    • pp.175-181
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    • 2021
  • This paper analyzes the artificial intelligence-based approach for short-term energy consumption prediction. In this paper, we employ the reinforcement learning algorithms to improve the limitation of the supervised learning algorithms which usually utilize to the short-term energy consumption prediction technologies. The supervised learning algorithm-based approaches have high complexity because the approaches require contextual information as well as energy consumption data for sufficient performance. We propose a deep reinforcement learning algorithm based on multi-agent to predict energy consumption only with energy consumption data for improving the complexity of data and learning models. The proposed scheme is simulated using public energy consumption data and confirmed the performance. The proposed scheme can predict a similar value to the actual value except for the outlier data.

An Action Research for the Practical Construction of the Constructivist Geography Education I (구성주의 지리교육의 실천적 구성을 위한 현장 연구 I)

  • 송언근
    • Journal of the Korean Geographical Society
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    • v.35 no.4
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    • pp.565-583
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    • 2000
  • 레지오 접근법을 토대로 한 구성주의적 지리교육이 현실적 적실성을 갖기 위해서는, 첫째, 도식적 언어를 통한 상징적 표상을 지식 구성의 결과가 아닌 과정적 도구로, 또는 토론의 매개체로 전환하여야 한다. 둘째, 주제(선도) 개념을 중심으로 연계성과 위계성을 가진 개념을 조직하고, 이를 토대로 여러 차시의 내용을 연계하여 수업해야한다. 섯째, 개인적 구성보다 사회적 구성이 보다 고차적이고 심층적인 지식 구성을 가능케 한다. 따라서 구성주의 수업은 협동적.토론적인 모습일 때가 바람직하다. 넷째, 수업의 방향성과 학습의 목적성을 위해 안내자, 조력자로서 교사의 역할을 다하여야 한다. 이때 안내는 학습자 스스로 자신의 학습 목적과 방향을 판단하는 매개체로서, 그리고 사고력을 증진시키는 디딤돌로써의 안내이어야 한다. 다섯째. 선행학습에서의 구성과정과 구성맥락을 재 상기시키고, 이를 통해 구서의 지속성과 심층성을 강화하는 나선형적 구성의 절차를 반드시 밟도록 해야 한다.

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Machine Scheduling Models Based on Reinforcement Learning for Minimizing Due Date Violation and Setup Change (납기 위반 및 셋업 최소화를 위한 강화학습 기반의 설비 일정계획 모델)

  • Yoo, Woosik;Seo, Juhyeok;Kim, Dahee;Kim, Kwanho
    • The Journal of Society for e-Business Studies
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    • v.24 no.3
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    • pp.19-33
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    • 2019
  • Recently, manufacturers have been struggling to efficiently use production equipment as their production methods become more sophisticated and complex. Typical factors hindering the efficiency of the manufacturing process include setup cost due to job change. Especially, in the process of using expensive production equipment such as semiconductor / LCD process, efficient use of equipment is very important. Balancing the tradeoff between meeting the deadline and minimizing setup cost incurred by changes of work type is crucial planning task. In this study, we developed a scheduling model to achieve the goal of minimizing the duedate and setup costs by using reinforcement learning in parallel machines with duedate and work preparation costs. The proposed model is a Deep Q-Network (DQN) scheduling model and is a reinforcement learning-based model. To validate the effectiveness of our proposed model, we compared it against the heuristic model and DNN(deep neural network) based model. It was confirmed that our proposed DQN method causes less due date violation and setup costs than the benchmark methods.

Result Analysis of Training Programs for Stengthening Teacher's ICT Competency (교원 정보화 역량강화 연수 운영 성과분석 연구)

  • Suh, Soon-Shik;Kim, Sung-Wan
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.10
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    • pp.111-120
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    • 2011
  • The purpose of this study is to analyze the results of training programs for strengthening teachers' ICT competency and to suggest the ways to improve the existing ones. To do this, we conducted literature review, web survey with teachers, and in-depth interview with training program operators. The results of the online survey indicated that generally speaking, the programs had been operated desirably, although three programs(Thinking with Technology Course, Essential Course, e-PBL Instructional Design Course) needed to take additional efforts for their effectiveness and efficiency. According to the results of in-depth interview with training operators, they demanded re-training for lecturers, self career development, quality control of regional delivery training, expansion of training periods, and post-supports after training etc.