← Back to all news
arXiv Machine Learning By Ruyi Tang (LCQB-AG), Gr\'egoire Sergeant-Perthuis (LCQB-AG), David Colliaux

Implementation of reinforcement learning in chemical reaction networks: application to phototaxis as curiosity-driven exploration

Read the original on arXiv Machine Learning →

arXiv:2606. 26168v1 Announce Type: new Abstract: Living systems navigate environments using noisy and incomplete sensory signals.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Sep 11

Bio-inspired Learning and Decision-Making with Probabilistic In-Memory Computing Hardware: Part 1

The article explores how biological learning and decision-making, often modeled as Bayesian processes, can be replicated in computing systems by leveraging noisy neural and synaptic dynamics for stochastic sampling. It proposes a biologically grounded framework where internal energy functions capture uncertainty over latent states and model parameters, enabling predictive coding networks to perform Markov chain Monte Carlo sampling. By drawing parallels between intrinsic biological noise and electrical noise in emerging probabilistic analogue memory technologies, the authors argue that analogue in‑memory computing hardware offers a massively scalable and energy‑efficient solution for probabilistic inference.

By Thomas Dalgaty, Eiji Kawasaki, Miguel de Prado, Devendra Vyas, Tommaso Salvatori
More like this →
arXiv Machine Learning
Sep 23

Simulation-free Structure Learning for Stochastic Population Dynamics

arXiv:2510.16656v2 Announce Type: replace Abstract: Modeling dynamical systems and unraveling their underlying structural dependencies is central to many domains in the natural sciences. Various phys...

By Noah El Rimawi-Fine, Adam Stecklov, Lucas Nelson, Mathieu Blanchette, Alexander Tong, Stephen Y. Zhang, Lazar Atanackovic
More like this →
arXiv Machine Learning
Jun 10

Rare Event Analysis via Stochastic Optimal Control

arXiv:2604. 13213v2 Announce Type: replace-cross Abstract: Rare events such as conformational changes in biomolecules, phase transitions, and chemical reactions are central to the behavior of many physical systems, yet they are extremely difficult to study computationally because unbiased simulations seldom produce them.

By Yuanqi Du, Jiajun He, Dinghuai Zhang, Eric Vanden-Eijnden, Carles Domingo-Enrich
benchmarks
More like this →