9 citations · 24 across the 13 of their papers we have counts for
15 papers
Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm
Colin Bellinger, Laurence Lamarche-Cliche
As technology progresses, industrial and scientific robots are increasingly being used in diverse settings. In many cases, however, programming the robot to perform such tasks is t…
Dynamic Observation Policies in Observation Cost-Sensitive Reinforcement Learning
Colin Bellinger, Mark Crowley, Isaac Tamblyn
Reinforcement learning (RL) has been shown to learn sophisticated control policies for complex tasks including games, robotics, heating and cooling systems and text generation. The…
ChemGymRL: An Interactive Framework for Reinforcement Learning for Digital Chemistry
Chris Beeler, Sriram Ganapathi Subramanian, Kyle Sprague +10
This paper provides a simulated laboratory for making use of Reinforcement Learning (RL) for chemical discovery. Since RL is fairly data intensive, training agents `on-the-fly' by…
Reinforcement Learning-based Wavefront Sensorless Adaptive Optics Approaches for Satellite-to-Ground Laser Communication
Payam Parvizi, Runnan Zou, Colin Bellinger +2
Optical satellite-to-ground communication (OSGC) has the potential to improve access to fast and affordable Internet in remote regions. Atmospheric turbulence, however, distorts th…
Interpretable ML for Imbalanced Data
Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk +2
Deep learning models are being increasingly applied to imbalanced data in high stakes fields such as medicine, autonomous driving, and intelligence analysis. Imbalanced data compou…
Understanding CNN Fragility When Learning With Imbalanced Data
Damien Dablain, Kristen N. Jacobson, Colin Bellinger +2
Convolutional neural networks (CNNs) have achieved impressive results on imbalanced image data, but they still have difficulty generalizing to minority classes and their decisions…