9 code implementations • 23 Nov 2016 • Vernon J. Lawhern, Amelia J. Solon, Nicholas R. Waytowich, Stephen M. Gordon, Chou P. Hung, Brent J. Lance
We introduce the use of depthwise and separable convolutions to construct an EEG-specific model which encapsulates well-known EEG feature extraction concepts for BCI.
1 code implementation • 12 Mar 2018 • Nicholas R. Waytowich, Vernon Lawhern, Javier O. Garcia, Jennifer Cummings, Josef Faller, Paul Sajda, Jean M. Vettel
Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli.
1 code implementation • 28 Aug 2018 • Nicholas R. Waytowich, Vinicius G. Goecks, Vernon J. Lawhern
We discuss different types of human-robot interaction paradigms in the context of training end-to-end reinforcement learning algorithms.
no code implementations • 13 Sep 2018 • Sean L. Barton, Nicholas R. Waytowich, Derrik E. Asher
We discuss the role of coordination as a direct learning objective in multi-agent reinforcement learning (MARL) domains.
Multi-agent Reinforcement Learning reinforcement-learning +1
1 code implementation • 26 Oct 2018 • Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern, John Valasek, Nicholas R. Waytowich
This paper investigates how to utilize different forms of human interaction to safely train autonomous systems in real-time by learning from both human demonstrations and interventions.
no code implementations • 11 Sep 2019 • Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich, Julie A. Shah
Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 9 Oct 2019 • Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern, John Valasek, Nicholas R. Waytowich
However, it is currently unclear how to efficiently update that policy using reinforcement learning as these approaches are inherently optimizing different objective functions.
no code implementations • 1 Nov 2019 • Ritwik Bera, Vinicius G. Goecks, Gregory M. Gremillion, John Valasek, Nicholas R. Waytowich
Learning from demonstration has been widely studied in machine learning but becomes challenging when the demonstrated trajectories are unstructured and follow different objectives.
1 code implementation • 25 Feb 2021 • Ravi Kumar Thakur, MD-Nazmus Samin Sunbeam, Vinicius G. Goecks, Ellen Novoseller, Ritwik Bera, Vernon J. Lawhern, Gregory M. Gremillion, John Valasek, Nicholas R. Waytowich
In this work, we propose Gaze Regularized Imitation Learning (GRIL), a novel context-aware, imitation learning architecture that learns concurrently from both human demonstrations and eye gaze to solve tasks where visual attention provides important context.
no code implementations • 29 Jun 2023 • Vinicius G. Goecks, Nicholas R. Waytowich
The development of plans of action in disaster response scenarios is a time-consuming process.
no code implementations • CVPR 2023 • Sean Kulinski, Nicholas R. Waytowich, James Z. Hare, David I. Inouye
Spatial reasoning tasks in multi-agent environments such as event prediction, agent type identification, or missing data imputation are important for multiple applications (e. g., autonomous surveillance over sensor networks and subtasks for reinforcement learning (RL)).