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
Monto Carlo Tree Search in Real Time Strategy Games with Applications to Starcraft 2
KTH, School of Electrical Engineering and Computer Science (EECS).
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis presents an architecture for an agent that can play the real-time strategy game Starcraft 2 (SC2) by applying Monte Carlo Tree Search (MCTS) together with genetic algorithms and machine learning methods. Together with the MCTS search, a light-weight and accurate combat simulator for SC2 as well as a build order optimizer are presented as independent modules. While MCTS has been well studied for turn-based games such as Go and Chess, its performance has so far been less explored in the context of real-time games. Using machine learning and planning methods in real-time strategy games without requiring long training times has proven to be a challenge. This thesis explores how a model based approach, based on the rules of the game, can be used to achieve a well performing agent.

Abstract [sv]

Denna uppsats presenterar en arkitektur för ett program som kan spela realtidsspelet Starcraft 2 (SC2) genom att använda Monte Carlo Tree Search (MCTS) tillsammans med genetiska algoritmer och maskininlärningsmetoder. Tillsammans med MCTS-sökningen så presenteras också en snabb och exakt stridssimulator för SC2 samt en optimeringsalgoritm för bygg-ordningar som separata moduler. MCTS has studerats mycket inom turordningsbaserade spel som till exempel Go och Schack, däremot så har det utforskats mindre när det kommer till realtidsspel. Att använda maskininlärning och planeringsalgoritmer i realtidsstrategispel utan att kräva långa träningstider har visat sig vara en utmaning. Denna uppsats utforskar hur ett modellbaserat tillvägagångssätt, baserat på reglerna för spelet, kan användas för att skapa ett bra presterande program.

Place, publisher, year, edition, pages
2019. , p. 46
Series
TRITA-EECS-EX ; 2019:546
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-263150OAI: oai:DiVA.org:kth-263150DiVA, id: diva2:1366883
Supervisors
Examiners
Available from: 2019-11-18 Created: 2019-10-31 Last updated: 2019-11-18Bibliographically approved

Open Access in DiVA

fulltext(4318 kB)8 downloads
File information
File name FULLTEXT01.pdfFile size 4318 kBChecksum SHA-512
36c6ee020f1fb189df7624838e5538ecaa7adfc7025c0421b92a4f147f1682ef6eab2e69152318eea538df535bad9d608671c448a8f09de4f251b764c7e10a32
Type fulltextMimetype application/pdf

By organisation
School of Electrical Engineering and Computer Science (EECS)
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 8 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: 24 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