100 citations · 251 across the 21 of their papers we have counts for
4 papers · 1 filter
Human irrationality: both bad and good for reward inference
Lawrence Chan, Andrew Critch, Anca Dragan
Assuming humans are (approximately) rational enables robots to infer reward functions by observing human behavior. But people exhibit a wide array of irrationalities, and our goal…
Quantifying Local Specialization in Deep Neural Networks
Shlomi Hod, Daniel Filan, Stephen Casper +2
A neural network is locally specialized to the extent that parts of its computational graph (i.e. structure) can be abstractly represented as performing some comprehensible sub-tas…
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Michael Dennis, Natasha Jaques, Eugene Vinitsky +4
A wide range of reinforcement learning (RL) problems - including robustness, transfer learning, unsupervised RL, and emergent complexity - require specifying a distribution of task…
The MAGICAL Benchmark for Robust Imitation
Sam Toyer, Rohin Shah, Andrew Critch +1
Imitation Learning (IL) algorithms are typically evaluated in the same environment that was used to create demonstrations. This rewards precise reproduction of demonstrations in on…