4.8k citations · 5.3k across the 23 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces
Garrett Warnell, Nicholas Waytowich, Vernon Lawhern +1
While recent advances in deep reinforcement learning have allowed autonomous learning agents to succeed at a variety of complex tasks, existing algorithms generally require a lot o…