Publications (5)
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…
Attention-Based Structural-Plasticity
Soheil Kolouri, Nicholas Ketz, Xinyun Zou +2
Catastrophic forgetting/interference is a critical problem for lifelong learning machines, which impedes the agents from maintaining their previously learned knowledge while learni…
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…
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…
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…