Diffusion for World Modeling: Visual Details Matter in Atari
Authors: name: Eloi Alonso, name: Adam Jelley, name: Vincent Micheli, name: Anssi Kanervisto, name: Amos Storkey, name: Tim Pearce, name: François Fleuret

World models constitute a promising approach for training reinforcement
learning agents in a safe and sample-efficient manner. Recent world models
predominantly operate on sequences of discrete latent variables to model
environment dynamics. However, this compression into a compact discrete
representation may ignore visual details that are important for reinforcement
learning. Concurrently, diffusion models have become a dominant approach for
image generation, challenging well-established methods modeling discrete
latents. Motivated by this paradigm shift, we introduce DIAMOND (DIffusion As a
Model Of eNvironment Dreams), a reinforcement learning agent trained in a
diffusion world model. We analyze the key design choices that are required to
make diffusion suitable for world modeling, and demonstrate how improved visual
details can lead to improved agent performance. DIAMOND achieves a mean human
normalized score of 1.46 on the competitive Atari 100k benchmark; a new best
for agents trained entirely within a world model. To foster future research on
diffusion for world modeling, we release our code, agents and playable world
models at https://github.com/eloialonso/diamond.