arXiv Artificial Intelligence

Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning

2 hours ago 01.10.2026 07:00 👁 2 views
Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning

Quick summary

arXiv:2512.02019v4 Announce Type: replace-cross Abstract: Diffusion models provide an expressive framework for sampling from complex, unnormalized distributions. In this work, we extend Maximum Entropy Reinforcement Learning (ME-RL) to diffusion-based policies by introducing Diffusion-Augmented Markov Decision Processes (DA-MDPs). DA-MDPs interpret each reverse-diffusion transition as an individual reinforcement-learning decision, while only the final denoised action is executed in the environment. Our DA-MDPs follow from a principled derivation based on the variational-inference formulation o

Key takeaways

  • arXiv:2512.02019v4 Announce Type: replace-cross Abstract: Diffusion models provide an expressive framework for sampling from complex, unnormalized distributions.
  • In this work, we extend Maximum Entropy Reinforcement Learning (ME-RL) to diffusion-based policies by introducing Diffusion-Augmented Markov Decision Processes (DA-MDPs).
  • DA-MDPs interpret each reverse-diffusion transition as an individual reinforcement-learning decision, while only the final denoised action is executed in the environment.

Why it matters

“Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗