Journal of Korea Game Society (한국게임학회 논문지)

Korea Game Society (한국게임학회)
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Reinforcement Learning-based Approach for Lego Puzzle Generation

강화학습을 이용한 레고 퍼즐 생성 기술 개발

  • 박철성 (상명대학교 컴퓨터과학과) ;
  • 양희경 (상명대학교 SW융합학부) ;
  • 민경하 (상명대학교 컴퓨터과학과)
  • Received : 2020.03.10
  • Accepted : 2020.06.08
  • Published : 2020.06.20
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Abstract

We present a reinforcement learning-based framework for generating 2D Lego puzzle from input pixel art images. We devise heuristics for a proper Lego puzzle as stability and efficiency. We also design a DQN structure and train it to maximize the heuristics of 2D Lego puzzle. In legorization stage, we complete the layout of Lego puzzle by adding a Lego brick to the input image using the trained DQN. During this process, we devise a region of interest to reduce the computational loads of the legorization. Using this approach, our framework can present a very high resolutional Lego puzzle.

2D 레고 퍼즐은 레고 브릭을 이용해서 다양한 영상을 완성하는 퍼즐로 많은 사람들의 사랑을 받고 있다. 본 연구에서는 입력된 픽셀 아트 영상으로부터 강화학습에 기반한 2D 레고 퍼즐을 구성하고 완성하는 방법을 제안한다. 먼저, 학습 단계에서는 바람직한 레고 퍼즐에 대한 휴리스틱을 안정성과 효율성으로 설정하고 이를 최대한 만족시키는 방향으로 DQN을 학습한다. 그리고, 레고화 단계에서는 이 DQN을 이용해서 실제 레고 브릭을 추가해가면서 퍼즐의 구성도를 완성하는 과정을 수행한다. 이 과정을 통해서 지금까지 기술로는 수행하기 힘들었던 매우 높은 해상도의 레고 퍼즐을 효율적으로 완성하는 기술을 제공한다.

Keywords

References

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