31 citations · 95 across the 18 of their papers we have counts for
19 papers
ATLAS: Adaptive Topological Learning with Abstract Successors for Continual Learning
R. Blake Lawlor, Daniel S. Brown
Contemporary model-free reinforcement learning algorithms can achieve very high performance, but have low sample efficiency and are not robust to changes in the environment. Model-…
Contextual Reliability: When Different Features Matter in Different Contexts
Gaurav Ghosal, Amrith Setlur, Daniel S. Brown +2
Deep neural networks often fail catastrophically by relying on spurious correlations. Most prior work assumes a clear dichotomy into spurious and reliable features; however, this i…
Benchmarks and Algorithms for Offline Preference-Based Reward Learning
Daniel Shin, Anca D. Dragan, Daniel S. Brown
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…
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models
Yi Liu, Gaurav Datta, Ellen Novoseller +1
Preference-based reinforcement learning (PbRL) can enable robots to learn to perform tasks based on an individual's preferences without requiring a hand-crafted reward function. Ho…
SIRL: Similarity-based Implicit Representation Learning
Andreea Bobu, Yi Liu, Rohin Shah +2
When robots learn reward functions using high capacity models that take raw state directly as input, they need to both learn a representation for what matters in the task -- the ta…
Learning Representations that Enable Generalization in Assistive Tasks
Jerry Zhi-Yang He, Aditi Raghunathan, Daniel S. Brown +2
Recent work in sim2real has successfully enabled robots to act in physical environments by training in simulation with a diverse ''population'' of environments (i.e. domain randomi…