arXiv:2512.02019v3 Announce Type: replace Abstract: Diffusion models excel at sampling from complex, unnormalized distributions. In this work, we extend Maximum Entropy Reinforcement Learning (ME-RL) to diffusion processes, enabling sampling from the optimal policy trajectory distribution. By minimizing a tractable upper bound on the reverse KL divergence between the diffusion policy and the optimal policy trajectory distributions, we derive a modified surrogate objective and introduce Diffusion-Augmented Markov Decision Processes (DA-MDPs). DA-MDPs allow for seamless integration of diffusion
This research builds on recent advances in diffusion models and reinforcement learning, integrating them to address complex sampling problems in optimal policy generation.
It introduces a novel framework that could significantly improve the efficiency and capability of AI agents to learn sophisticated behaviors in complex environments.
The ability to sample from optimal policy trajectory distributions via diffusion processes changes how advanced AI systems could be designed and trained.
- · AI researchers
- · Reinforcement learning platforms
- · Robotics
- · Traditional RL methods
Improved performance and sample efficiency in various AI agent applications.
Accelerated development of more adaptive and robust autonomous systems.
Potential for new categories of AI-driven services and products that require nuanced decision-making.
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