• 제목/요약/키워드: Reinforcement learning

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Path Planning of Unmanned Aerial Vehicle based Reinforcement Learning using Deep Q Network under Simulated Environment (시뮬레이션 환경에서의 DQN을 이용한 강화 학습 기반의 무인항공기 경로 계획)

  • Lee, Keun Hyoung;Kim, Shin Dug
    • Journal of the Semiconductor & Display Technology
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    • v.16 no.3
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    • pp.127-130
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    • 2017
  • In this research, we present a path planning method for an autonomous flight of unmanned aerial vehicles (UAVs) through reinforcement learning under simulated environment. We design the simulator for reinforcement learning of uav. Also we implement interface for compatibility of Deep Q-Network(DQN) and simulator. In this paper, we perform reinforcement learning through the simulator and DQN, and use Q-learning algorithm, which is a kind of reinforcement learning algorithms. Through experimentation, we verify performance of DQN-simulator. Finally, we evaluated the learning results and suggest path planning strategy using reinforcement learning.

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Traffic-based reinforcement learning with neural network algorithm in fog computing environment

  • Jung, Tae-Won;Lee, Jong-Yong;Jung, Kye-Dong
    • International Journal of Internet, Broadcasting and Communication
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    • v.12 no.1
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    • pp.144-150
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    • 2020
  • Reinforcement learning is a technology that can present successful and creative solutions in many areas. This reinforcement learning technology was used to deploy containers from cloud servers to fog servers to help them learn the maximization of rewards due to reduced traffic. Leveraging reinforcement learning is aimed at predicting traffic in the network and optimizing traffic-based fog computing network environment for cloud, fog and clients. The reinforcement learning system collects network traffic data from the fog server and IoT. Reinforcement learning neural networks, which use collected traffic data as input values, can consist of Long Short-Term Memory (LSTM) neural networks in network environments that support fog computing, to learn time series data and to predict optimized traffic. Description of the input and output values of the traffic-based reinforcement learning LSTM neural network, the composition of the node, the activation function and error function of the hidden layer, the overfitting method, and the optimization algorithm.

Online Reinforcement Learning to Search the Shortest Path in Maze Environments (미로 환경에서 최단 경로 탐색을 위한 실시간 강화 학습)

  • Kim, Byeong-Cheon;Kim, Sam-Geun;Yun, Byeong-Ju
    • The KIPS Transactions:PartB
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    • v.9B no.2
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    • pp.155-162
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    • 2002
  • Reinforcement learning is a learning method that uses trial-and-error to perform Learning by interacting with dynamic environments. It is classified into online reinforcement learning and delayed reinforcement learning. In this paper, we propose an online reinforcement learning system (ONRELS : Outline REinforcement Learning System). ONRELS updates the estimate-value about all the selectable (state, action) pairs before making state-transition at the current state. The ONRELS learns by interacting with the compressed environments through trial-and-error after it compresses the state space of the mage environments. Through experiments, we can see that ONRELS can search the shortest path faster than Q-learning using TD-ewor and $Q(\lambda{)}$-learning using $TD(\lambda{)}$ in the maze environments.

Topic directed Web Spidering using Reinforcement Learning (강화학습을 이용한 주제별 웹 탐색)

  • Lim, Soo-Yeon
    • Journal of the Korean Institute of Intelligent Systems
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    • v.15 no.4
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    • pp.395-399
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    • 2005
  • In this paper, we presents HIGH-Q learning algorithm with reinforcement learning for more fast and exact topic-directed web spidering. The purpose of reinforcement learning is to maximize rewards from environment, an reinforcement learning agents learn by interacting with external environment through trial and error. We performed experiments that compared the proposed method using reinforcement learning with breath first search method for searching the web pages. In result, reinforcement learning method using future discounted rewards searched a small number of pages to find result pages.

A Study on Performance Improvement of Evolutionary Algorithms Using Reinforcement Learning (강화학습을 이용한 진화 알고리즘의 성능개선에 대한 연구)

  • 이상환;심귀보
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.420-426
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    • 1998
  • Evolutionary algorithms are probabilistic optimization algorithms based on the model of natural evolution. Recently the efforts to improve the performance of evolutionary algorithms have been made extensively. In this paper, we introduce the research for improving the convergence rate and search faculty of evolution algorithms by using reinforcement learning. After providing an introduction to evolution algorithms and reinforcement learning, we present adaptive genetic algorithms, reinforcement genetic programming, and reinforcement evolution strategies which are combined with reinforcement learning. Adaptive genetic algorithms generate mutation probabilities of each locus by interacting with the environment according to reinforcement learning. Reinforcement genetic programming executes crossover and mutation operations based on reinforcement and inhibition mechanism of reinforcement learning. Reinforcement evolution strategies use the variances of fitness occurred by mutation to make the reinforcement signals which estimate and control the step length.

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Reinforcement Learning using Propagation of Goal-State-Value (목표상태 값 전파를 이용한 강화 학습)

  • Kim, Byeong-Cheon;Yun, Byeong-Ju
    • The Transactions of the Korea Information Processing Society
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    • v.6 no.5
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    • pp.1303-1311
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    • 1999
  • In order to learn in dynamic environments, reinforcement learning algorithms like Q-learning, TD(0)-learning, TD(λ)-learning have been proposed. however, most of them have a drawback of very slow learning because the reinforcement value is given when they reach their goal state. In this thesis, we have proposed a reinforcement learning method that can approximate fast to the goal state in maze environments. The proposed reinforcement learning method is separated into global learning and local learning, and then it executes learning. Global learning is a learning that uses the replacing eligibility trace method to search the goal state. In local learning, it propagates the goal state value that has been searched through global learning to neighboring sates, and then searches goal state in neighboring states. we can show through experiments that the reinforcement learning method proposed in this thesis can find out an optimal solution faster than other reinforcement learning methods like Q-learning, TD(o)learning and TD(λ)-learning.

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Credit-Assigned-CMAC-based Reinforcement Learning with application to the Acrobot Swing Up Control Problem (Acrobot Swing Up 제어를 위한 Credit-Assigned-CMAC 기반의 강화학습)

  • Shin, Yeon-Yong;Jang, Si-Young;Seo, Seung-Hwan;Suh, Il-Hong
    • Proceedings of the KIEE Conference
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    • pp.621-624
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    • 2003
  • For real world applications of reinforcement learning techniques, function approximation or generalization will be required to avoid curse of dimensionality. For this, an improved function approximation-based reinforcement learning method is proposed to speed up convergence by using CA-CMAC(Credit-Assigned Cerebellar Model Articulation Controller). To show that our proposed CACRL(CA-CMAC-based Reinforcement Learning) performs better than the CRL(CMAC-based Reinforcement Learning), computer simulation results are illustrated, where a swing-up control problem of an acrobot is considered.

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A Survey on Deep Reinforcement Learning Libraries (심층강화학습 라이브러리 기술동향)

  • Shin, S.J.;Cho, C.L.;Jeon, H.S.;Yoon, S.H.;Kim, T.Y.
    • Electronics and Telecommunications Trends
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    • v.34 no.6
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    • pp.87-99
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    • 2019
  • Reinforcement learning is a type of machine learning paradigm that forces agents to repeat the observation-action-reward process to assess and predict the values of possible future action sequences. This allows the agents to incrementally reinforce the desired behavior for a given observation. Thanks to the recent advancements of deep learning, reinforcement learning has evolved into deep reinforcement learning that introduces promising results in various control and optimization domains, such as games, robotics, autonomous vehicles, computing, industrial control, and so on. In addition to this trend, a number of programming libraries have been developed for importing deep reinforcement learning into a variety of applications. In this article, we briefly review and summarize 10 representative deep reinforcement learning libraries and compare them from a development project perspective.

A Function Approximation Method for Q-learning of Reinforcement Learning (강화학습의 Q-learning을 위한 함수근사 방법)

  • 이영아;정태충
    • Journal of KIISE:Software and Applications
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    • v.31 no.11
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    • pp.1431-1438
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    • 2004
  • Reinforcement learning learns policies for accomplishing a task's goal by experience through interaction between agent and environment. Q-learning, basis algorithm of reinforcement learning, has the problem of curse of dimensionality and slow learning speed in the incipient stage of learning. In order to solve the problems of Q-learning, new function approximation methods suitable for reinforcement learning should be studied. In this paper, to improve these problems, we suggest Fuzzy Q-Map algorithm that is based on online fuzzy clustering. Fuzzy Q-Map is a function approximation method suitable to reinforcement learning that can do on-line teaming and express uncertainty of environment. We made an experiment on the mountain car problem with fuzzy Q-Map, and its results show that learning speed is accelerated in the incipient stage of learning.

Design of Multiobjective Satisfactory Fuzzy Logic Controller using Reinforcement Learning

  • Kang, Dong-Oh;Zeungnam Bien
    • Proceedings of the IEEK Conference
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    • pp.677-680
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    • 2000
  • The technique of reinforcement learning algorithm is extended to solve the multiobjective control problem for uncertain dynamic systems. A multiobjective adaptive critic structure is proposed in order to realize a max-min method in the reinforcement learning process. Also, the proposed reinforcement learning technique is applied to a multiobjective satisfactory fuzzy logic controller design in which fuzzy logic subcontrollers are assumed to be derived from human experts. Some simulation results are given in order to show effectiveness of the proposed method.

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