Improving Transformer World Models for Data-Efficient RL
Authors: name: Antoine Dedieu, name: Joseph Ortiz, name: Xinghua Lou, name: Carter Wendelken, name: Wolfgang Lehrach, name: J Swaroop Guntupalli, name: Miguel Lazaro-Gredilla, name: Kevin Patrick Murphy

We present an approach to model-based RL that achieves a new state of the art
performance on the challenging Craftax-classic benchmark, an open-world 2D
survival game that requires agents to exhibit a wide range of general abilities
-- such as strong generalization, deep exploration, and long-term reasoning.
With a series of careful design choices aimed at improving sample efficiency,
our MBRL algorithm achieves a reward of 67.4% after only 1M environment steps,
significantly outperforming DreamerV3, which achieves 53.2%, and, for the first
time, exceeds human performance of 65.0%. Our method starts by constructing a
SOTA model-free baseline, using a novel policy architecture that combines CNNs
and RNNs. We then add three improvements to the standard MBRL setup: (a) "Dyna
with warmup", which trains the policy on real and imaginary data, (b) "nearest
neighbor tokenizer" on image patches, which improves the scheme to create the
transformer world model (TWM) inputs, and (c) "block teacher forcing", which
allows the TWM to reason jointly about the future tokens of the next timestep.