Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning

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

Source: arXiv cs.LG — read the full report at the original publisher.

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