Hugging Face Trending Papers
Sep 8

Routing Dense Layouts with History-Aware Offline Reinforcement Learning using LSTM

The paper introduces a history‑aware offline reinforcement learning policy that predicts iterative cost weights for routing in dense integrated circuit designs. By incorporating a lightweight LSTM and additional router features, the policy retains sequence context and improves convergence across various placement densities and guide qualities. Integrated into any cost‑based router with minimal changes, the approach reduces design rule violations by an average of 92% and cuts runtime by 10%.

arXiv AI
Aug 25

Memory-Enhanced Neural Solvers for Routing Problems

The paper introduces MEMENTO, a memory‑enhanced neural solver that improves routing problem solutions by using online data from repeated attempts to adjust action distributions during inference. It targets NP‑hard routing tasks such as the Traveling Salesman and Capacitated Vehicle Routing problems, outperforming existing tree‑search and policy‑gradient fine‑tuning methods. MEMENTO demonstrates strong scalability and data efficiency, achieving state‑of‑the‑art results on 11 of 12 evaluated tasks and enabling zero‑shot integration with diversity‑based solvers.

By Felix Chalumeau, Refiloe Shabe, Noah De Nicola, Arnu Pretorius, Thomas D. Barrett, Nathan Grinsztajn
arXiv AI
Jul 21

A Deep Reinforcement Learning Algorithm for the Vehicle Routing Problem with Stochastic Demands and Outsourcing

arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.

By Mohsen Dastpak, Fausto Errico, Ola Jabali