• Title/Summary/Keyword: Reinforcement learning

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Enhanced Machine Learning Algorithms: Deep Learning, Reinforcement Learning, and Q-Learning

  • Park, Ji Su;Park, Jong Hyuk
    • Journal of Information Processing Systems
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    • v.16 no.5
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    • pp.1001-1007
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    • 2020
  • In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

Credit-Assigned-CMAC-based Reinforcement Learn ing with Application to the Acrobot Swing Up Control Problem (Acrobot Swing Up Control을 위한 Credit-Assigned-CMAC-based 강화학습)

  • 장시영;신연용;서승환;서일홍
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.53 no.7
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    • pp.517-524
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    • 2004
  • 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 teaming 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 and experiment results are illustrated, where a swing-up control Problem of an acrobot is considered.

Fuzzy Inferdence-based Reinforcement Learning for Recurrent Neural Network (퍼지 추론에 의한 리커런트 뉴럴 네트워크 강화학습)

  • 전효병;이동욱;김대준;심귀보
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.120-123
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    • 1997
  • In this paper, we propose the Fuzzy Inference-based Reinforcement Learning Algorithm. We offer more similar learning scheme to the psychological learning of the higher animal's including human, by using Fuzzy Inference in Reinforcement Learning. The proposed method follows the way linguistic and conceptional expression have an effect on human's behavior by reasoning reinforcement based on fuzzy rule. The intervals of fuzzy membership functions are found optimally by genetic algorithms. And using Recurrent state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying to the inverted pendulum control problem.

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Performance Improvement of Genetic Algorithms by Reinforcement Learning (강화학습을 통한 유전자 알고리즘의 성능개선)

  • 이상환;전효병;심귀보
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.81-84
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    • 1998
  • Genetic Algorithms (GAs) are stochastic algorithms whose search methods model some natural phenomena. The procedure of GAs may be divided into two sub-procedures : Operation and Selection. Chromosomes can produce new offspring by means of operation, and the fitter chromosomes can produce more offspring than the less fit ones by means of selection. However, operation which is executed randomly and has some limits to its execution can not guarantee to produce fitter chromosomes. Thus, we propose a method which gives a directional information to the genetic operator by reinforcement learning. It can be achived by using neural networks to apply reinforcement learning to the genetic operator. We use the amount of fitness change which can be considered as reinforcement signal to calcualte the error terms for the output units. Then the weights are updated using backpropagtion algorithm. The performance improvement of GAs using reinforcement learning can be measured by applying the pr posed method to GA-hard problem.

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Controller Learning Method of Self-driving Bicycle Using State-of-the-art Deep Reinforcement Learning Algorithms

  • Choi, Seung-Yoon;Le, Tuyen Pham;Chung, Tae-Choong
    • Journal of the Korea Society of Computer and Information
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    • v.23 no.10
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    • pp.23-31
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    • 2018
  • Recently, there have been many studies on machine learning. Among them, studies on reinforcement learning are actively worked. In this study, we propose a controller to control bicycle using DDPG (Deep Deterministic Policy Gradient) algorithm which is the latest deep reinforcement learning method. In this paper, we redefine the compensation function of bicycle dynamics and neural network to learn agents. When using the proposed method for data learning and control, it is possible to perform the function of not allowing the bicycle to fall over and reach the further given destination unlike the existing method. For the performance evaluation, we have experimented that the proposed algorithm works in various environments such as fixed speed, random, target point, and not determined. Finally, as a result, it is confirmed that the proposed algorithm shows better performance than the conventional neural network algorithms NAF and PPO.

GAN-based Color Palette Extraction System by Chroma Fine-tuning with Reinforcement Learning

  • Kim, Sanghyuk;Kang, Suk-Ju
    • Journal of Semiconductor Engineering
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    • v.2 no.1
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    • pp.125-129
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    • 2021
  • As the interest of deep learning, techniques to control the color of images in image processing field are evolving together. However, there is no clear standard for color, and it is not easy to find a way to represent only the color itself like the color-palette. In this paper, we propose a novel color palette extraction system by chroma fine-tuning with reinforcement learning. It helps to recognize the color combination to represent an input image. First, we use RGBY images to create feature maps by transferring the backbone network with well-trained model-weight which is verified at super resolution convolutional neural networks. Second, feature maps are trained to 3 fully connected layers for the color-palette generation with a generative adversarial network (GAN). Third, we use the reinforcement learning method which only changes chroma information of the GAN-output by slightly moving each Y component of YCbCr color gamut of pixel values up and down. The proposed method outperforms existing color palette extraction methods as given the accuracy of 0.9140.

A Study on Deep Reinforcement Learning Framework for DME Pulse Design

  • Lee, Jungyeon;Kim, Euiho
    • Journal of Positioning, Navigation, and Timing
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    • v.10 no.2
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    • pp.113-120
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    • 2021
  • The Distance Measuring Equipment (DME) is a ground-based aircraft navigation system and is considered as an infrastructure that ensures resilient aircraft navigation capability during the event of a Global Navigation Satellite System (GNSS) outage. The main problem of DME as a GNSS back up is a poor positioning accuracy that often reaches over 100 m. In this paper, a novel approach of applying deep reinforcement learning to a DME pulse design is introduced to improve the DME distance measuring accuracy. This method is designed to develop multipath-resistant DME pulses that comply with current DME specifications. In the research, a Markov Decision Process (MDP) for DME pulse design is set using pulse shape requirements and a timing error. Based on the designed MDP, we created an Environment called PulseEnv, which allows the agent representing a DME pulse shape to explore continuous space using the Soft Actor Critical (SAC) reinforcement learning algorithm.

Performance Enhancement of CSMA/CA MAC Protocol Based on Reinforcement Learning

  • Kim, Tae-Wook;Hwang, Gyung-Ho
    • Journal of information and communication convergence engineering
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    • v.19 no.1
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    • pp.1-7
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    • 2021
  • Reinforcement learning is an area of machine learning that studies how an intelligent agent takes actions in a given environment to maximize the cumulative reward. In this paper, we propose a new MAC protocol based on the Q-learning technique of reinforcement learning to improve the performance of the IEEE 802.11 wireless LAN CSMA/CA MAC protocol. Furthermore, the operation of each access point (AP) and station is proposed. The AP adjusts the value of the contention window (CW), which is the range for determining the backoff number of the station, according to the wireless traffic load. The station improves the performance by selecting an optimal backoff number with the lowest packet collision rate and the highest transmission success rate through Q-learning within the CW value transmitted from the AP. The result of the performance evaluation through computer simulations showed that the proposed scheme has a higher throughput than that of the existing CSMA/CA scheme.

High-Accuracy Motion Control of Linear Synchronous Motor Using Reinforcement Learning (강화학습에 의한 선형동기 모터의 고정밀 제어)

  • Jeong, Seong-Hyen;Park, Jung-Il
    • Journal of the Korean Society for Precision Engineering
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    • v.28 no.12
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    • pp.1379-1387
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    • 2011
  • A PID-feedforward controller and Robust Internal-loop Compensator (RIC) based on reinforcement learning using random variable sequences are provided to auto-tune parameters for each controller in the high-precision position control of PMLSM (Permanent Magnet Linear Synchronous Motor). Experiments prove the well-tuned controller could be reduced up to one-fifth level of tracking errors before learning by reinforcement learning. The RIC compared to the PID-feedforward controller showed approximately twice the performance in reducing tracking error and disturbance rejection.

A Study of Adaptive QoS Routing scheme using Policy-gradient Reinforcement Learning (정책 기울기 값 강화학습을 이용한 적응적인 QoS 라우팅 기법 연구)

  • Han, Jeong-Soo
    • Journal of the Korea Society of Computer and Information
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    • v.16 no.2
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    • pp.93-99
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    • 2011
  • In this paper, we propose a policy-gradient routing scheme under Reinforcement Learning that can be used adaptive QoS routing. A policy-gradient RL routing can provide fast learning of network environments as using optimal policy adapted average estimate rewards gradient values. This technique shows that fast of learning network environments results in high success rate of routing. For prove it, we simulate and compare with three different schemes.