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arXiv · 2402.03570

Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning

Zihan Ding·Amy Zhang·Yuandong Tian·Qinqing Zheng
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Abstract

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.

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Zihan Ding, Amy Zhang, Yuandong Tian, Qinqing Zheng. 2024-02-05. Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning. https://arxiv.org/abs/2402.03570

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