Mastering Diverse Domains through World Models

General intelligence requires solving tasks across many domains. Current reinforcement learning algorithms carry this potential but are held back by the resources and knowledge required to tune them for new tasks. We present DreamerV3, a general and scalable algorithm based on world models that outperforms previous approaches across a wide range of domains with fixed hyperparameters. These domains include continuous and discrete actions, visual and low-dimensional inputs, 2D and 3D worlds, different data budgets, reward frequencies 2023: Danijar Hafner, J. Pašukonis, Jimmy Ba, T. Lillicrap https://arxiv.org/pdf/2301.04104v1.pdf