1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 1 cited
A Study of Plasticity Loss in On-Policy Deep Reinforcement Learning
Arthur Juliani, Jordan T. Ash
Continual learning with deep neural networks presents challenges distinct from both the fixed-dataset and convex continual learning regimes. One such challenge is plasticity loss,…
cs.LG2022★ 1 cited
A Biologically-Inspired Dual Stream World Model
Arthur Juliani, Margaret Sereno
The medial temporal lobe (MTL), a brain region containing the hippocampus and nearby areas, is hypothesized to be an experience-construction system in mammals, supporting both reca…
cs.NE2022
Neuro-Nav: A Library for Neurally-Plausible Reinforcement Learning
Arthur Juliani, Samuel Barnett, Brandon Davis +2
In this work we propose Neuro-Nav, an open-source library for neurally plausible reinforcement learning (RL). RL is among the most common modeling frameworks for studying decision…