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SNES Games

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크
0
평가표
5
보존된 평가 기록
15

The task is to train an agent to play SNES games such as Super Mario.

<span style="color:grey; opacity: 0.6">( Image credit: Large-Scale Study of Curiosity-Driven Learning )</span>

데이터셋

출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.

Mario AI

Mario AI was a benchmark environment for reinforcement learning. The gameplay in Mario AI, as in the original Nintendo’s version, consists in moving the controlled character, namely Mario, through two-dimensional levels, which are viewed sideways. Mario can walk and run to the right and left, jump, and (depending on which state he is in) shoot fireballs. Gravity acts on Mario, making it necessary to jump over cliffs to get past them. Mario can be in one of three states: Small, Big (can kill enemies by jumping onto them), and Fire (can shoot fireballs). Source: https://github.com/zerg000000/mario-ai Image Source: http://julian.togelius.com/Karakovskiy2012The.pdf

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.