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Diffusion World Model: Future Modeling Beyond Step-by-Step Rollout for Offline Reinforcement Learning (2402.03570v4)

Published 5 Feb 2024 in cs.LG and cs.AI

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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Citations (12)
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Summary

  • The paper introduces Diffusion World Model, a conditional diffusion approach that predicts multi-step future states in one pass to mitigate error accumulation.
  • It demonstrates a 44% performance gain over traditional one-step dynamics models by utilizing synthetic trajectories for enhanced value estimation.
  • Experimental results on the D4RL dataset confirm its robust long-horizon simulation, effectively bridging model-based and model-free RL methods.

Introduction

The field of Reinforcement Learning (RL) has been largely divided into two primary strategies: model-based (MB) and model-free (MF) approaches. MB RL relies on predictive models to simulate an environment's feedback system, which is beneficial in terms of sample efficiency but has often faced performance challenges due to compounding errors in model predictions. Recent advances, however, have leveraged sequence modeling techniques to proffer solutions to decision-making problems in RL. This has led to the investigation of whether sequence modeling can effectively reduce long-horizon prediction errors and enhance the performance of MBRL algorithms, a key focus of this paper.

Diffusion World Model

A distinct approach, introduced in this work, is the Diffusion World Model (DWM), which predicts future states and rewards over multiple steps directly in one forward pass. Unlike traditional models requiring recursive predictions, DWM reduces the accumulation of modeling errors. DWM has been integrated into model-based RL algorithms, particularly for value estimation. In offline RL settings, DWM operates by creating future trajectories which are utilized for simulating short-term returns, thus serving as a generative modeling-based value regularization technique or an enabler for offline Q-learning with synthetic data. Notably, DWM demonstrates a 44% performance gain when compared to one-step dynamics models, achieving state-of-the-art results.

Experimental Validation

Experiments on the D4RL dataset validate DWM's robustness to long-horizon simulation across numerous locomotion tasks. These experiments benchmark both sequence-level world models, including transformers and diffusion-based models, and traditional one-step models. The consistent outperformance of the DWM signals a pivotal shift in the capabilities of world models within MBRL, marking significant progress in addressing modeling errors that plague long-horizon predictions.

Interpretation and Insights

DWM's ability to simulate extended future states shines a light on diverse strategic advantages within the offline RL framework. It is likened to a conservative value regularization technique and, alternatively, as a method to perform offline Q-learning with synthetic data. This multifaceted functionality speaks not only to the flexibility of DWM but also to its conceptual significance in bridging the gap between MB and MF methods.

Conclusion

The introduction of Diffusion World Model as a conditional diffusion model for MBRL is a noteworthy advancement that showcases remarkable efficiency in long-horizon predictions and significant performance improvements over traditional one-step models. The promising results obtained through rigorous experimentation advocate for further research into DWM's applicability in various RL contexts, including its potential in online settings and its computational demands. The implications of such an approach could extend well into practical applications where MBRL has been traditionally employed, such as robotics, autonomous systems, and other domains where decision-making under uncertainty is critical.

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