arXiv AI
Oct 1

Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning

The paper introduces Diffusion-Augmented Markov Decision Processes (DA‑MDPs), a framework that extends Maximum Entropy Reinforcement Learning to diffusion-based policies. DA‑MDPs treat each reverse‑diffusion step as an RL decision, deriving a tractable reverse‑KL bound that decomposes across denoising transitions and yields diffusion‑augmented soft rewards, value functions, and policy objectives. The authors implement this framework with PPO, REPPO, and a maximum‑entropy WPO variant, showing improved continuous‑control performance, higher success rates on manipulation tasks, and memory‑efficient training with action chunking.

By Sebastian Sanokowski, Kaustubh Patil, Majid Khadiv
arXiv AI
Oct 12

Stochastic MeanFlow Policies: One-Step Generative Control with Entropic Mirror Descent

The paper introduces Stochastic MeanFlow Policies (SMFP), a one‑step generative policy class that merges MeanFlow noise‑to‑action mappings with Gaussian reparameterisation. SMFP captures diverse action modes while providing an analytic entropy lower bound through a learnable conditional Gaussian scale, enabling entropy‑regularised mirror‑descent learning. Experiments on seven MuJoCo benchmarks show SMFP outperforming Gaussian and generative baselines, and additional tests demonstrate effective multimodal action modelling and exploration in sparse‑reward navigation.

By Zeyuan Wang, Da Li, Yulin Chen, Yuehu Gong, Yanming Guo, Liang Bai, Tianyuan Yu, Yanwei Fu