  We introduce Diffusion World Model (DWM), a conditional diffusion model
capable of predicting multistep future states and rewards concurrently. As
opposed to traditional one-step dynamics models, DWM offers long-horizon
predictions in a single forward pass, eliminating the need for recursive
queries. We integrate DWM into model-based value estimation, where the
short-term return is simulated by future trajectories sampled from DWM. In the
context of offline reinforcement learning, DWM can be viewed as a conservative
value regularization through generative modeling. Alternatively, it can be seen
as a data source that enables offline Q-learning with synthetic data. Our
experiments on the D4RL dataset confirm the robustness of DWM to long-horizon
simulation. In terms of absolute performance, DWM significantly surpasses
one-step dynamics models with a $44\%$ performance gain, and is comparable to
or slightly surpassing their model-free counterparts.
