Simple, Good, Fast: Self-Supervised World Models Free of Baggage
Jan Robine Marc Höftmann Stefan Harmeling
What are the essential components of world models? How far do we get with world models that are not employing RNNs, transformers, discrete representations, and image reconstructions? This paper introduces SGF, a Simple, Good, and Fast world model that uses self-supervised representation learning, captures short-time dependencies through frame and action stacking, and enhances robustness against model errors through data augmentation. We extensively discuss SGF’s connections to established world models, evaluate the building blocks in ablation studies, and demonstrate good performance through quantitative comparisons on the Atari 100k benchmark. The code is available at https://github.com/jrobine/sgf.
Reinforcement learning, World models, Self-supervised learning, Atari 100k
What are the essential components of world models? We present a simple, good, and fast world model, and evaluate it on the Atari 100k benchmark.