태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Montezuma's Revenge is an ATARI 2600 Benchmark game that is known to be difficult to perform on for reinforcement learning algorithms. Solutions typically employ algorithms that incentivise environment exploration in different ways.
For the state-of-the art tables, please consult the parent Atari Games task.
<span style="color:grey; opacity: 0.6">( Image credit: Q-map )</span>
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
The Arcade Learning Environment (ALE) is an object-oriented framework that allows researchers to develop AI agents for Atari 2600 games. It is built on top of the Atari 2600 emulator Stella and separates the details of emulation from agent design. Source: https://github.com/mgbellemare/Arcade-Learning-Environment Image Source: https://github.com/muupan/async-rl/blob/master/README.md
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.