nach oben

Soft Computing

Erschienen in:

06.03.2020 | Focus

Improving decision-making efficiency of image game based on deep Q-learning

verfasst von: Zhe Ji, Wenjun Xiao

Erschienen in: Soft Computing | Ausgabe 11/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

To promote effective decision-making in video games and win high scores in a short time, the deep learning algorithms are integrated into game image processing for reinforcement learning. By changing the mapping function from priority to probability, a deep Q-learning priority experience replay algorithm is deduced, which is then compared with the single mapping function. Various researches have proved that the improved algorithm can reproduce the mapping function with higher probability of playback learning of the unit. The advantage of the agent is that it can master the most complete game strategy and ultimately obtain higher scores with the help of the strategy. Therefore, the proposed algorithm is to help the agent formulate a more useful strategy when playing video games. On the one hand, the agent can get better game records. On the other hand, the energy consumed in the game is greatly reduced.

Vorheriger Artikel RETRACTED ARTICLE: Prediction research of financial time series based on deep learning

Nächster Artikel Prediction of fundraising outcomes for crowdfunding projects based on deep learning: a multimodel comparative study

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Barhatov V, Campa A, Pletnev D (2018) The impact of internet-technologies development on small business success in Russia. Proc Soc Behav Sci 238:552–561CrossRef

Barriga Nicolas A, Stanescu Marius, Besoain Felipe et al (2019) Improving RTS game AI by supervised policy learning, tactical search, and deep reinforcement learning. IEEE Comput Intell Mag 14(3):11CrossRef

Chen XL, Cao L, Li CX et al (2018) Deep reinforcement learning via good choice resampling experience replay memory. Kongzhi Yu Juece control Decis 33(4):600–606MATH

Chiu C, Liu TK, Lu WT et al (2018) A micro-control capture images technology for the finger vein recognition based on adaptive image segmentation. Microsyst Technol 10:1–14

Folkes SR, Lahav O, Maddox SJ (2018) An artificial neural network approach to the classification of galaxy spectra. Mon Not R Astron Soc 283(2):651–665CrossRef

Geng L, Dong T (2017) An agricultural monitoring system based on wireless sensor and depth learning algorithm. Int J Online Biomed Eng (iJOE) 13(12):127–137CrossRef

He Y, Zhang Z, Richard Yu F et al (2017) Deep reinforcement learning-based optimization for cache-enabled opportunistic interference alignment wireless networks. IEEE Trans Vehic Technol 66(11):10433–10445CrossRef

Heo YJ, Kim SJ, Kim D et al (2018) Super-high-purity seed sorter using low-latency image-recognition based on deep learning. IEEE Robot Autom Lett 3(4):3035–3042CrossRef

Hu Z, Tong H, Zeng Y et al (2018) Fast image recognition of transmission tower based on big data. Prot Control Mod Power Syst 3(1):15CrossRef

Lcc E, Kutafina E, Jonas SM (2018) Automatic recognition of epileptiform EEG abnormalities. Stud Health Technol Inform 247:171–175

Li Y, Li Y (2017) Face recognition algorithm based on sparse representation of DAE convolution neural network. Recent Patents Comput Sci 10(4):290–298CrossRef

Li H, Shi Y, Zhang B et al (2018a) superpixel-based feature for aerial image scene recognition. Sensors 18(1):156CrossRef

Li Y, Zhang HK, Xue XZ et al (2018b) Deep learning for remote sensing image classification: a survey. Wiley Interdiscip Rev Data Min Knowl Discov 12:e1264

Lin M, Sarkar M, Mukherjee A et al (2017) Introspection: accelerating neural network training by learning weight evolution. arXiv preprint arXiv:1704.04959

Liu LB, Hodgins J (2017) Learning to schedule control fragments for physics-based characters using deep Q-learning. Acm Trans Gr 36(4):1CrossRef

Poon P (2017) Modeling and simulation for exploring power/time trade-off of parallel deep neural network training. Proc Comput Sci 108:2463–2467CrossRef

Qi X (2018) Rotor resistance and excitation inductance estimation of an induction motor using deep-Q-learning algorithm. Eng Appl Artif Intell 72:67–79CrossRef

Shen Y, Zhao N, Xia M et al (2017) A deep q-learning network for ship stowage planning problem. Pol Marit Res 24(s3):102–109CrossRef

Su Y, Liu J, Lin D (2018) Wrong matching points elimination after scale invariant feature transform and its application to image matching. Pattern Recognit Image Anal 28(1):87–96CrossRef

Wu W, Li AD, He XH et al (2018) A comparison of support vector machines, artificial neural network and classification tree for identifying soil texture classes in southwest China. Comput Electron Agric 144:86–93CrossRef

Xu H, Han Z, Feng S et al (2018) Foreign object debris material recognition based on convolutional neural networks. Eurasip J Image Video Process 2018(1):21CrossRef

Zhang Q, Man L, Yang LT et al (2017) Energy-efficient scheduling for real-time systems based on deep Q-learning model. IEEE Trans Sustain Comput 4(99):1

Zhao L, Wang JD, Liu JJ et al (2019) Routing for crowd management in smart cities: a deep reinforcement learning perspective. IEEE Commun Mag 57(4):88–93CrossRef

Zhou Z, Huang G, Chen H et al (2018) Automatic radar waveform recognition based on deep convolutional denoising auto-encoders. Circuits Syst Signal Process 37(9):4034–4048MathSciNetCrossRef

Zhu J, Song Y, Jiang D et al (2017) A new deep-Q-learning-based transmission scheduling mechanism for the cognitive internet of things. IEEE Intern Things J 5(4):2375–2385CrossRef

Titel: Improving decision-making efficiency of image game based on deep Q-learning
verfasst von: Zhe Ji
Wenjun Xiao
Publikationsdatum: 06.03.2020
Verlag: Springer Berlin Heidelberg
Erschienen in: Soft Computing / Ausgabe 11/2020
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-020-04820-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 11/2020

Scheduling two-stage assembly flow shop with random machines breakdowns: integrated new self-adapted differential evolutionary and simulation approach

Image semantic segmentation with an improved fully convolutional network

An intelligent and generic approach for detecting human emotions: a case study with facial expressions

Modeling and simulation of novel dynamic control strategy for PV–wind hybrid power system using FGS−PID and RBFNSM methods

A new fuzzy time series method based on an ARMA-type recurrent Pi-Sigma artificial neural network

A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine