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Volumn , Issue , 2011, Pages 85-90

Learning policies for first person shooter games using inverse reinforcement learning

Author keywords

[No Author keywords available]

Indexed keywords

FIRST PERSON SHOOTER; FIRST PERSON SHOOTER GAMES; HUMAN DEMONSTRATIONS; INTERNAL MODELING; INVERSE REINFORCEMENT LEARNING; LEARNING POLICY; UNREAL TOURNAMENT; WORLD KNOWLEDGE;

EID: 84875634501     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (47)

References (11)
  • 2
    • 72449184029 scopus 로고    scopus 로고
    • Pogamut 3 can assist developers in building AI (not only) for their videogame agents
    • Dignum, F. Bradshaw, J. Silverman, B. and van Doesburg, W. eds., Lecture Notes in Computer Science. Springer Berlin/Heidelberg
    • Gemrot, J.; Kadlec, R.; Bida, M.; Burkert, O.; Pibil, R.; Havlcek, J.; Zemck, L.; Lovic, J.; Vansa, R.; Tolba, M.; Plch, T.; and Brom, C. 2009. Pogamut 3 can assist developers in building AI (not only) for their videogame agents. In Dignum, F.; Bradshaw, J.; Silverman, B.; and van Doesburg, W., eds., Agents for Games and Simulations, Lecture Notes in Computer Science. Springer Berlin/Heidelberg.
    • (2009) Agents for Games and Simulations
    • Gemrot, J.1    Kadlec, R.2    Bida, M.3    Burkert, O.4    Pibil, R.5    Havlcek, J.6    Zemck, L.7    Lovic, J.8    Vansa, R.9    Tolba, M.10    Plch, T.11    Brom, C.12


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.