AMAF 휴리스틱을 적용한 삼목게임
(주)코리아스칼라
- 최초 등록일
- 2023.04.05
- 최종 저작일
- 2017.06
- 7페이지/ 어도비 PDF
- 가격 4,000원
* 본 문서는 배포용으로 복사 및 편집이 불가합니다.
서지정보
ㆍ발행기관 : 한국컴퓨터게임학회
ㆍ수록지정보 : 한국컴퓨터게임학회 논문지 / 30권 / 2호
ㆍ저자명 : 이병두
목차
ABSTRACT
1. Introduction
2. Body
3. Experimental Results
4. Conclusion
4. 결론
Reference
영어 초록
Monte-Carlo Tree Search (MCTS) is a best-first search algorithm to evaluate states of the game tree in game playing, and has been successfully applied to various games, especially to the game of Go. Upper Confidence Bounds for Trees (UCT), which is a variant of MCTS, uses the UCB1 formula as selection policy, and balances exploitation and exploration of the states. Rapid Action-Value Estimation (RAVE), which is a All-Moves-As-First (AMAF) heuristic, treats all moves in a simulation as the first move, and therefore updates the statistics of all children of the root node. In this paper, we evaluate the performance of RAVE and UCT playing against each other in the game of Tic-Tac-Toe. The experimental results show that the first player RAVE is much inferior to the second player UCT (13.0±0.7%); on the other hand, the first player UCT is far superior to RAVE (99.9±0.1%).
참고 자료
없음
"한국컴퓨터게임학회 논문지"의 다른 논문
더보기 (5/10)