91 citations · 102 across the 5 of their papers we have counts for
5 papers
Time-Efficient Reward Learning via Visually Assisted Cluster Ranking
David Zhang, Micah Carroll, Andreea Bobu +1
One of the most successful paradigms for reward learning uses human feedback in the form of comparisons. Although these methods hold promise, human comparison labeling is expensive…
UniMASK: Unified Inference in Sequential Decision Problems
Micah Carroll, Orr Paradise, Jessy Lin +8
Randomly masking and predicting word tokens has been a successful approach in pre-training language models for a variety of downstream tasks. In this work, we observe that the same…
Optimal Behavior Prior: Data-Efficient Human Models for Improved Human-AI Collaboration
Mesut Yang, Micah Carroll, Anca Dragan
AI agents designed to collaborate with people benefit from models that enable them to anticipate human behavior. However, realistic models tend to require vast amounts of human dat…
Evaluating the Robustness of Collaborative Agents
Paul Knott, Micah Carroll, Sam Devlin +4
In order for agents trained by deep reinforcement learning to work alongside humans in realistic settings, we will need to ensure that the agents are \emph{robust}. Since the real…
On the Utility of Learning about Humans for Human-AI Coordination
Micah Carroll, Rohin Shah, Mark K. Ho +4
While we would like agents that can coordinate with humans, current algorithms such as self-play and population-based training create agents that can coordinate with themselves. Ag…