Mastering Diverse Domains through World Models

arXiv:2301.04104v2 Announce Type: replace-cross
Abstract: Developing a general algorithm that learns to solve tasks across a wide range of applications has been a fundamental challenge in artificial intelligence. Although current reinforcement learning algorithms can be readily applied to tasks similar to what they have been developed for, configuring them for new application domains requires significant human expertise and experimentation. We present DreamerV3, a general algorithm that outperforms specialized methods across over 150 diverse tasks, with a single configuration. Dreamer learns …

abstract algorithm algorithms application applications artificial artificial intelligence arxiv challenge cs.ai cs.lg current diverse domains general intelligence reinforcement reinforcement learning solve stat.ml tasks them through type world world models