91 citations · 96 across the 4 of their papers we have counts for
13 papers
2021 BEETL Competition: Advancing Transfer Learning for Subject Independence & Heterogenous EEG Data Sets
Xiaoxi Wei, A. Aldo Faisal, Moritz Grosse-Wentrup +18
Transfer learning and meta-learning offer some of the most promising avenues to unlock the scalability of healthcare and consumer technologies driven by biosignal data. This is bec…
A Narration-based Reward Shaping Approach using Grounded Natural Language Commands
Nicholas Waytowich, Sean L. Barton, Vernon Lawhern +1
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniq…
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments
Vinicius G. Goecks, Gregory M. Gremillion, Vernon J. Lawhern +2
This paper investigates how to efficiently transition and update policies, trained initially with demonstrations, using off-policy actor-critic reinforcement learning. It is well-k…
Grounding Natural Language Commands to StarCraft II Game States for Narration-Guided Reinforcement Learning
Nicholas Waytowich, Sean L. Barton, Vernon Lawhern +2
While deep reinforcement learning techniques have led to agents that are successfully able to learn to perform a number of tasks that had been previously unlearnable, these techniq…
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…
Collaborative Brain-Computer Interface for Human Interest Detection in Complex and Dynamic Settings
Amelia J. Solon, Stephen M. Gordon, Jonathan R. McDaniel +1
Humans can fluidly adapt their interest in complex environments in ways that machines cannot. Here, we lay the groundwork for a real-world system that passively monitors and merges…