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Gaze-Informed Multi-Objective Imitation Learning from Human Demonstrations


Feb 25, 2021
Ritwik Bera, Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern, John Valasek, Nicholas R. Waytowich


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PODNet: A Neural Network for Discovery of Plannable Options


Nov 15, 2019
Ritwik Bera, Vinicius G. Goecks, Gregory M. Gremillion, John Valasek, Nicholas R. Waytowich


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Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Sparse Reward Environments


Oct 09, 2019
Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern, John Valasek, Nicholas R. Waytowich

* 9 pages, 3 Figures 

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On Memory Mechanism in Multi-Agent Reinforcement Learning


Sep 11, 2019
Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich, Julie A. Shah


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On the use of Deep Autoencoders for Efficient Embedded Reinforcement Learning


Mar 25, 2019
Bharat Prakash, Mark Horton, Nicholas R. Waytowich, William David Hairston, Tim Oates, Tinoosh Mohsenin


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Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time


Nov 28, 2018
Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern, John Valasek, Nicholas R. Waytowich

* 9 pages, 6 figures 

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Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials


Oct 09, 2018
Nicholas R. Waytowich, Vernon Lawhern, Javier O. Garcia, Jennifer Cummings, Josef Faller, Paul Sajda, Jean M. Vettel

* Accepted for publication at the Journal of Neural Engineering 

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Cycle-of-Learning for Autonomous Systems from Human Interaction


Oct 09, 2018
Nicholas R. Waytowich, Vinicius G. Goecks, Vernon J. Lawhern

* Presented at AI-HRI AAAI-FSS, 2018 (arXiv:1809.06606) 

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Coordination-driven learning in multi-agent problem spaces


Sep 13, 2018
Sean L. Barton, Nicholas R. Waytowich, Derrik E. Asher

* AAAI Fall Symposium 2018, Concept Paper 

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EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces


May 16, 2018
Vernon J. Lawhern, Amelia J. Solon, Nicholas R. Waytowich, Stephen M. Gordon, Chou P. Hung, Brent J. Lance

* 30 pages, 10 figures. Added additional feature relevance analyses. Minor change to EEGNet architecture. Source code can be found at https://github.com/vlawhern/arl-eegmodels 

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