4.8k citations · 5.1k across the 44 of their papers we have counts for
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Efficiently Combining Human Demonstrations and Interventions for Safe Training of Autonomous Systems in Real-Time
Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern +2
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 intervent…
Coordination-driven learning in multi-agent problem spaces
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. To this end, we present a novel means of quantifying coordi…
Cycle-of-Learning for Autonomous Systems from Human Interaction
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. We provide a taxonomy to categorize the typ…
Measuring collaborative emergent behavior in multi-agent reinforcement learning
Sean L. Barton, Nicholas R. Waytowich, Erin Zaroukian +1
Multi-agent reinforcement learning (RL) has important implications for the future of human-agent teaming. We show that improved performance with multi-agent RL is not a guarantee o…
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
Nicholas R. Waytowich, Vernon Lawhern, Javier O. Garcia +4
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. SSVEPs a…