10 citations · 18 across the 5 of their papers we have counts for
5 papers
Statistical Context Detection for Deep Lifelong Reinforcement Learning
Jeffery Dick, Saptarshi Nath, Christos Peridis +3
Context detection involves labeling segments of an online stream of data as belonging to different tasks. Task labels are used in lifelong learning algorithms to perform consolidat…
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…
Context Meta-Reinforcement Learning via Neuromodulation
Eseoghene Ben-Iwhiwhu, Jeffery Dick, Nicholas A. Ketz +2
Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks from few samples in dynamic environments. Such a feat is achieved through dynamic represent…
Evolving Inborn Knowledge For Fast Adaptation in Dynamic POMDP Problems
Eseoghene Ben-Iwhiwhu, Pawel Ladosz, Jeffery Dick +3
Rapid online adaptation to changing tasks is an important problem in machine learning and, recently, a focus of meta-reinforcement learning. However, reinforcement learning (RL) al…
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…