arXiv Machine Learning
Aug 28

Simple Actors and Deep Critics for Scalable Reinforcement Learning

The paper introduces LAC (Light Actor, deep Critic), an offline reinforcement learning approach that allocates model capacity to a deep critic rather than a complex actor to improve inference efficiency. It addresses three failure modes—optimization, bootstrap-noise amplification, and value-range drift—using a residual MLP backbone, n‑step bootstrap targets, and a categorical cross‑entropy loss. Experiments on OGBench show LAC matches state‑of‑the‑art diffusion and flow‑matching baselines while reducing inference latency by up to four times.

By Guhyeon Kang, Jaehwi Lee, Minhae Kwon
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
arXiv Machine Learning
Sep 18

Improving Online Reinforcement Learning via Bidirectional Behavior Prior Distillation

The paper introduces Bidirectional Behavior Prior Distillation (B2PD), a method that uses action‑value priors to train a conditional variational autoencoder for generating high‑value behavior support. These expert behavior priors are then distilled into the online reinforcement learning agent, reducing inefficient exploration and stabilizing policy updates. Experiments on state‑ and pixel‑based tasks show that B2PD improves sample efficiency while maintaining stable learning dynamics.

By Gong Gao, Xiao Lai, Jiaji Shen, Ning Jia, Xianhui Liu, Weidong Zhao