42 citations · 146 across the 13 of their papers we have counts for
15 papers
Can Differentiable Decision Trees Enable Interpretable Reward Learning from Human Feedback?
Akansha Kalra, Daniel S. Brown
Reinforcement Learning from Human Feedback (RLHF) has emerged as a popular paradigm for capturing human intent to alleviate the challenges of hand-crafting the reward values. Despi…
Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms
Connor Mattson, Daniel S. Brown
Robot swarms often exhibit emergent behaviors that are fascinating to observe; however, it is often difficult to predict what swarm behaviors can emerge under a given set of agent…
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning
Tu Trinh, Haoyu Chen, Daniel S. Brown
We examine the problem of determining demonstration sufficiency: how can a robot self-assess whether it has received enough demonstrations from an expert to ensure a desired level…
Offline Preference-Based Apprenticeship Learning
Daniel Shin, Daniel S. Brown, Anca D. Dragan
Learning a reward function from human preferences is challenging as it typically requires having a high-fidelity simulator or using expensive and potentially unsafe actual physical…
Dynamically Switching Human Prediction Models for Efficient Planning
Arjun Sripathy, Andreea Bobu, Daniel S. Brown +1
As environments involving both robots and humans become increasingly common, so does the need to account for people during planning. To plan effectively, robots must be able to res…
Value Alignment Verification
Daniel S. Brown, Jordan Schneider, Anca D. Dragan +1
As humans interact with autonomous agents to perform increasingly complicated, potentially risky tasks, it is important to be able to efficiently evaluate an agent's performance an…