Hugging Face Trending Papers
Aug 20

Spike-based Belief Propagation in Nonlinear Dynamical Systems

The paper introduces a Bayesian control framework that merges spike-based neural dynamics with probabilistic inference for adaptive control. It applies this brain-inspired model to the mountain car parking problem, showing that the controller can update states in real time and generate goal-directed action plans via spike-driven dynamics. The results suggest the model could serve as a bridge between computational neuroscience and probabilistic control theory.

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
Aug 24

Behavior-Consistent Deep Reinforcement Learning

The paper introduces the concept of behavior-consistent deep reinforcement learning, aiming to produce high-performing policies that remain distributionally similar across different training runs. It shows that maximum-entropy RL can control behavioral divergence by anchoring runs to a common prior, and proves that for Boltzmann policies, a temperature proportional to Q‑function disagreement limits pairwise KL divergence. Building on this, the authors propose Q‑value Expectile Disagreement (QED), a state‑dependent temperature schedule that uses double‑critic disagreement to approximate cross‑run disagreement, and demonstrate that QED reduces across‑run divergence by two orders of magnitude on 18 continuous‑control tasks without sacrificing performance.

By Marcel Hussing, Liv G. d'Aliberti, Claas Voelcker, Benjamin Eysenbach, Eric Eaton
arXiv Machine Learning
Aug 6

Distributional Active Inference

arXiv:2601. 20985v2 Announce Type: replace Abstract: Optimal control of complex environments with robotic systems faces two complementary and intertwined challenges: efficient organization of sensory state information and far-sighted action planning.

By Abdullah Akg\"ul, Gulcin Baykal, Manuel Hau{\ss}mann, Mustafa Mert \c{C}elikok, Melih Kandemir