We present three improvements to the standard model-based RL paradigm based on transformers: (a) "Dyna with warmup", which trains the policy on real and imaginary data, but only starts using imaginary data after the world model has been sufficiently trained; (b) "nearest neighbor tokenizer" for image patches, which improves upon previous tokenization schemes, which are needed when using a transformer world model (TWM), by ensuring the code words are static after creation, thus providing a constant target for TWM learning; and (c) "block teacher forcing", which allows the TWM to reason jointly about the future tokens of the next timestep, instead of generating them sequentially. We then show that our method significantly improves upon prior methods in various environments. We mostly focus on the challenging Craftax-classic benchmark, where our method achieves a reward of 69.66% after only 1M environment steps, significantly outperforming DreamerV3, which achieves 53.2%, and exceeding human performance of 65.0% for the first time. We also show preliminary results on Craftax-full, MinAtar, and three different two-player games, to illustrate the generality of the approach.
@article{arxiv.2502.01591,
title = {Improving Transformer World Models for Data-Efficient RL},
author = {Antoine Dedieu and Joseph Ortiz and Xinghua Lou and Carter Wendelken and Wolfgang Lehrach and J Swaroop Guntupalli and Miguel Lazaro-Gredilla and Kevin Patrick Murphy},
journal= {arXiv preprint arXiv:2502.01591},
year = {2025}
}