Advantage-Guided Diffusion for Model-Based Reinforcement Learning

Researchers propose Advantage-Guided Diffusion (AGD-MBRL), a novel approach that improves model-based reinforcement learning by using advantage estimates to guide diffusion models during trajectory generation. The method addresses the short-horizon myopia problem in existing diffusion-based world models and demonstrates 2x performance improvements over current baselines on MuJoCo control tasks.

This research tackles a fundamental challenge in model-based reinforcement learning: the tension between joint trajectory generation and long-term optimality. Diffusion models for MBRL offer advantages over autoregressive approaches by generating trajectory segments simultaneously, reducing compounding errors. However, prior guidance mechanisms suffer from critical limitations—policy-only guides ignore value information, while reward-based guides optimize myopically within short windows, missing longer-term returns.

The AGD approach innovates by leveraging advantage estimates, a well-established concept in reinforcement learning that captures the relative value of state-action pairs beyond immediate rewards. By mathematically proving that advantage-guided diffusion enables reweighted trajectory sampling with weights proportional to advantage values, the authors connect diffusion model steering to policy improvement theory. This theoretical grounding distinguishes their work from heuristic alternatives.

The practical implications are substantial. Achieving 2x improvements over PolyGRAD and reward-based guides suggests the method resolves a genuine optimization bottleneck. The seamless integration with existing PolyGRAD architectures—guiding state generation while keeping action generation policy-conditioned—demonstrates pragmatic engineering that enables rapid adoption. The absence of training objective modifications further reduces implementation friction.

For the MBRL community, this work signals that advantage-aware guidance represents a more principled direction than reward-only alternatives. The experimental validation across multiple continuous control tasks shows generalization beyond toy problems. Future research likely explores scaling to higher-dimensional action spaces and hybrid approaches combining advantage and policy guidance for different trajectory components.

• →Advantage-Guided Diffusion steers diffusion models using advantage estimates to improve long-term return prediction in model-based RL.
• →The method theoretically guarantees policy improvement through reweighted trajectory sampling with advantage-proportional weights.
• →AGD-MBRL achieves 2x performance improvements over PolyGRAD, reward-guided diffusion, and model-free baselines on continuous control tasks.
• →The approach integrates seamlessly with existing architectures without modifying training objectives, enabling practical deployment.
• →Advantage-aware guidance solves short-horizon myopia in diffusion models by explicitly optimizing beyond immediate generated windows.