17 citations · 35 across the 7 of their papers we have counts for
5 papers · 1 filter
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems
Megan M. Baker, Alexander New, Mario Aguilar-Simon +44
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks enc…
Concept-modulated model-based offline reinforcement learning for rapid generalization
Nicholas A. Ketz, Praveen K. Pilly
The robustness of any machine learning solution is fundamentally bound by the data it was trained on. One way to generalize beyond the original training is through human-informed a…
An Online Data-Driven Emergency-Response Method for Autonomous Agents in Unforeseen Situations
Glenn Maguire, Nicholas Ketz, Praveen Pilly +1
Reinforcement learning agents perform well when presented with inputs within the distribution of those encountered during training. However, they are unable to respond effectively…
Deep Reinforcement Learning with Modulated Hebbian plus Q Network Architecture
Pawel Ladosz, Eseoghene Ben-Iwhiwhu, Jeffery Dick +6
This paper presents a new neural architecture that combines a modulated Hebbian network (MOHN) with DQN, which we call modulated Hebbian plus Q network architecture (MOHQA). The hy…
Continual Learning Using World Models for Pseudo-Rehearsal
Nicholas Ketz, Soheil Kolouri, Praveen Pilly
The utility of learning a dynamics/world model of the environment in reinforcement learning has been shown in a many ways. When using neural networks, however, these models suffer…