Papers SNES Games
“SNES Games” 태그가 달린 논문 4편 · 필터 해제
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
Being able to reach any desired location in the environment can be a valuable asset for an agent. Learning a policy to navigate between all pairs of states individually is often not feasible. An all-goals updating algori…
AllMontezuma's RevengeNavigateQ-Learning+5Large-Scale Study of Curiosity-Driven Learning
Reinforcement learning algorithms rely on carefully engineering environment rewards that are extrinsic to the agent. However, annotating each environment with hand-designed, dense rewards is not scalable, motivating the …
Atari GamesPredictionReinforcement LearningSNES GamesEvolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network
Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content Generation (PCG) of levels for video gam…
Generative Adversarial NetworkSNES GamesPlaying SNES in the Retro Learning Environment
Mastering a video game requires skill, tactics and strategy. While these attributes may be acquired naturally by human players, teaching them to a computer program is a far more challenging task. In recent years, extensi…
Atari GamesReinforcement LearningSNES Games