Experiment in Using Reinforcement Learning in Gaming: The Breakout Game Learning
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In this study, we investigated whether a tool such as a game toy can be used as an augmented reality tool, and a system model that can be extended to a game element using wireless communication technology such as Bluetooth and a controllable module. This is an online ship type game using augmented reality technology and wireless communication technology. In addition, the existing game element was extended by applying a smartphone app control module. The existing game method uses the method of playing the game with only limited functions in the same space. This study expands to augmented reality-based games by implementing contents in a way that matches game objects with the grafting of augmented reality technology, and uses various items that emerge as the limit of reality. Therefore, we standardized the size of game objects so that they can be used three-dimension in all spaces on the screen according to the space arrangement such as overlapping prevention, distance, and height, and augmented reality technology was used to allow the game to be played by manipulation of a smartphone. In addition, we propose a system framework-based model that can be applied to various games, and a framework that can implement various augmented reality environments. The augmented reality-based battle game proposed in this study combines a knowledge-based augmented reality system that can be extended to game elements by modularizing the function of a toy through+ a context-aware agent based on context information and an intelligent DB based on domain knowledge.
Abstract
In this study, we investigated whether a tool such as a game toy can be used as an augmented reality tool, and a system model that can be extended to a game element using wireless communication technology such as Bluetooth and a controllable module. This is an online ship type game using augmented reality technology and wireless communication technology. In addition, the existing game element was extended by applying a smartphone app control module. The existing game method uses the method of playing the game with only limited functions in the same space. This study expands to augmented reality-based games by implementing contents in a way that matches game objects with the grafting of augmented reality technology, and uses various items that emerge as the limit of reality. Therefore, we standardized the size of game objects so that they can be used three-dimension in all spaces on the screen according to the space arrangement such as overlapping prevention, distance, and height, and augmented reality technology was used to allow the game to be played by manipulation of a smartphone. In addition, we propose a system framework-based model that can be applied to various games, and a framework that can implement various augmented reality environments. The augmented reality-based battle game proposed in this study combines a knowledge-based augmented reality system that can be extended to game elements by modularizing the function of a toy through+ a context-aware agent based on context information and an intelligent DB based on domain knowledge.
참고문헌 (13)
- 정무식, "Designing and implementing casual game operations tools", 한국컴퓨터게임학회논문지, 34(1), pp.79~88 (2021), (사)한국컴퓨터게임학회
- 유철강, "아이트래킹을 이용한 모바일 게임의 장르별 집중도 평가방법에 관한 연구", 한국컴퓨터게임학회논문지, 28(2), pp.179~185 (2015), (사)한국컴퓨터게임학회
- Sutton, Richard S., "Reinforcement learning: An introduction" (2018), MIT press
- Gagniuc, Paul A, "Markov chains: from theory to implementation and experimentation" (2017), John Wiley & Sons
- Gerald Tesauro, "Temporal difference learning and td-gammon", Commu-nications of the ACM, 38(3), pp.58~68 (1995)
- Jordan B. Pollack, "Why did td-gammon work", Advances in Neural Information Processing Systems, 9, pp.10~16 (1996)
- Volodymyr Mn, "Human-level control through deep reinforcement learning", Nature, 518(7540), pp.529~533 (2015)[DOI]
- Silver, David, "Mastering the game of Go with deep neural networks and tree search", Nature, 529, pp.484~489 (2016)[DOI]
- Van Hasselt, H, "Deep reinforcement learning with double qlearning", Paper presented at the Thirtieth AAAI Conference on Artificial Intelligence (2016)
- Hado van Hasselt, "Deep reinforce- ment learning with double Q-Learning", Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI’16), pp.2094~2100 (2016), AAAI Press
- Patel, Devdhar, "Improved robustness of reinforce- ment learning policies upon conversion to spiking neuronal network platforms applied to Atari Breakout game", Neural Networks, 120 (2019)[DOI]
- Jeerige, Anoop, "Comparison of deep reinforcement learning approaches for intelligent game playing", 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC) (2019)
- Junpeng Wang, "DQNViz: A Visual Analytics Approach to Understand Deep Q- Networks", IEEE Transactions on Visualization &Computer Graphics, 25(1), pp.288~298 (2019)