Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Deep Imitation Learning on Spatio-Temporal Data with Multiple Adversarial Agents Applied on Soccer
Linköping University, Department of Computer and Information Science, Database and information techniques.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Recently, the availability of high quality and high resolution spatio-temporal data has increased for many sports. This enabled deep analysis of player behaviour and game strategy. This thesis investigates the assumption that game strategy is latent information in tracking data from soccer games and the possibility of modelling player behaviour with deep imitation learning. A possible application would be to perform counterfactual analysis, and switch an observed player in a real sequence, with a simulated player to asses alternative scenarios.

An imitation learning application is implemented using recurrent neural networks. It is shown that the application is able to learn individual player behaviour and perform rollouts on previously unseen sequences.

Place, publisher, year, edition, pages
2019. , p. 48
Keywords [en]
imitation learning, soccer, trajectory, deep learning, lstm
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-158076ISRN: LIU-IDA/LITH-EX-A--19/036--SEOAI: oai:DiVA.org:liu-158076DiVA, id: diva2:1329462
External cooperation
Signality AB
Subject / course
Information Technology
Supervisors
Examiners
Available from: 2019-08-06 Created: 2019-06-24 Last updated: 2019-08-06Bibliographically approved

Open Access in DiVA

fulltext(17812 kB)62 downloads
File information
File name FULLTEXT01.pdfFile size 17812 kBChecksum SHA-512
d196859f54e771cd0445e9b62bf63a41305279c2406622588d1a6f570e4b0539af56bef8305ae290829a02f8c07f7525a91e85e28afe532aa4a58397e211b823
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Lindström, Per
By organisation
Database and information techniques
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 62 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 82 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf