arXiv Statistics ML
1d ago

Learning to Plan from Random Exploration

arXiv:2609.38383v1 Announce Type: cross Abstract: Random exploration reveals how an environment can be traversed before a goal is specified. Can this experience support long-range planning without po...

By Deqian Kong, Guangyan Sun, Sheng Cheng, Sirui Xie, Bo Pang, Jianwen Xie, Tony Geng, Caiwen Ding, Ying Nian Wu
arXiv AI
Sep 25

ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks

The paper introduces ELiSe, a model that leverages cortical network scaffolds and dendritic compartments to learn complex non‑Markovian spatio‑temporal patterns using only local, always‑on, phase‑free synaptic plasticity. It demonstrates the model’s ability to acquire and replay intricate sequences, exemplified by a birdsong learning mock‑up, and shows robustness to external disturbances and flexibility in parameter settings.

By Laura Kriener, Kristin V\"olk, Ben von H\"unerbein, Federico Benitez, Walter Senn, Mihai A. Petrovici
arXiv AI
Sep 7

Compact Bellman-Grounded Cognitive Maps for Cost-Aware Navigation

Compact Bellman-Grounded Cognitive Maps (BCM) are introduced as a new method for cost-aware navigation that reuses a single learned map for different goals without per-goal retraining. BCM grounds the map in local edge costs using a self-supervised Bellman objective and a compact coordinate encoding, achieving near-optimal performance on weighted grids up to 1600 nodes with only a 5% mean gap to exact Dijkstra search. Its memory footprint grows sublinearly with graph size, outperforming connectivity-based spectral baselines and demonstrating scalability to complex environments.

By Yuzhe Han, Mingkun Xu, Yujie Wu