17 citations · 54 across the 14 of their papers we have counts for
11 papers · 1 filter
Competence-Aware AI Agents with Metacognition for Unknown Situations and Environments (MUSE)
Rodolfo Valiente, Praveen K. Pilly
Metacognition, defined as the awareness and regulation of one's cognitive processes, is central to human adaptability in unknown situations. In contrast, current autonomous agents…
The configurable tree graph (CT-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning
Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Christos Peridis +4
This paper introduces a set of formally defined and transparent problems for reinforcement learning algorithms with the following characteristics: (1) variable degrees of observabi…
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…
Lifelong Reinforcement Learning with Modulating Masks
Eseoghene Ben-Iwhiwhu, Saptarshi Nath, Praveen K. Pilly +2
Lifelong learning aims to create AI systems that continuously and incrementally learn during a lifetime, similar to biological learning. Attempts so far have met problems, includin…
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…
Lifelong Learning with Sketched Structural Regularization
Haoran Li, Aditya Krishnan, Jingfeng Wu +3
Preventing catastrophic forgetting while continually learning new tasks is an essential problem in lifelong learning. Structural regularization (SR) refers to a family of algorithm…
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…
Generative Continual Concept Learning
Mohammad Rostami, Soheil Kolouri, James McClelland +1
After learning a concept, humans are also able to continually generalize their learned concepts to new domains by observing only a few labeled instances without any interference wi…
Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay
Mohammad Rostami, Soheil Kolouri, Praveen K. Pilly
Despite huge success, deep networks are unable to learn effectively in sequential multitask learning settings as they forget the past learned tasks after learning new tasks. Inspir…
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…