Journal of the Korea Computer Graphics Society (한국컴퓨터그래픽스학회논문지)

Korea Computer Graphics Society (한국컴퓨터그래픽스학회)
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Learning Multi-Character Competition in Markov Games

마르코프 게임 학습에 기초한 다수 캐릭터의 경쟁적 상호작용 애니메이션 합성

  • 이강훈 (광운대학교 컴퓨터소프트웨어학과)
  • Published : 2009.06.01
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Abstract

Animating multiple characters to compete with each other is an important problem in computer games and animation films. However, it remains difficult to simulate strategic competition among characters because of its inherent complex decision process that should be able to cope with often unpredictable behavior of opponents. We adopt a reinforcement learning method in Markov games to action models built from captured motion data. This enables two characters to perform globally optimal counter-strategies with respect to each other. We also extend this method to simulate competition between two teams, each of which can consist of an arbitrary number of characters. We demonstrate the usefulness of our approach through various competitive scenarios, including playing-tag, keeping-distance, and shooting.

다수 캐릭터가 경쟁적으로 상호작용하는 애니메이션의 합성은 컴퓨터 게임, 애니메이션 등의 응용분야에서 종종 요구되는 중요한 문제이다. 하지만 상대의 예측하기 어려운 행동에 효과적으로 대응하는 전략적 경쟁 양상을 모사하는 것은 어려운 문제로 남아있다. 본 논문은 다수 에이전트 학습 분야에서 제안된 마르코프 게임 강화학습 알고리즘을 촬영된 동작 데이터로부터 생성된 행위 모델에 적용하여 사실적인 경쟁 애니메이션을 합성하는 방식을 제안한다. 추격-회피, 간격 유지, 총격전 등의 다양한 경쟁적 상황에 대하여 효과적인 전략을 학습하여 흥미로운 애니메이션을 합성하는 예제들을 통하여 본 논문이 제안하는 방법의 효용성을 보인다.

Keywords

References

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