Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning

📰 ArXiv cs.AI

Learn how to apply diffusion models to Maximum Entropy Reinforcement Learning for improved policy trajectory sampling

advanced Published 28 May 2026

Action Steps

Apply diffusion models to Markov Decision Processes to enable sampling from complex distributions
Derive a modified surrogate objective to minimize the reverse KL divergence between the diffusion policy and optimal policy trajectory distributions
Use the diffusion-augmented MDP framework to improve exploration and sampling in Maximum Entropy Reinforcement Learning
Implement the Dif-ME-RL algorithm to optimize the policy trajectory distribution
Evaluate the performance of Dif-ME-RL using metrics such as cumulative reward and entropy

Who Needs to Know This

Researchers and engineers working on reinforcement learning and diffusion models can benefit from this technique to improve policy sampling and exploration

Key Insight

💡 Diffusion models can be used to augment Markov Decision Processes for improved policy trajectory sampling in Maximum Entropy Reinforcement Learning

Full Article

Title: Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning

Abstract:
arXiv:2512.02019v3 Announce Type: replace-cross 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 Dif

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