91 citations · 189 across the 19 of their papers we have counts for
5 papers · 1 filter
The MineRL BASALT Competition on Learning from Human Feedback
Rohin Shah, Cody Wild, Steven H. Wang +10
The last decade has seen a significant increase of interest in deep learning research, with many public successes that have demonstrated its potential. As such, these systems are n…
Learning What To Do by Simulating the Past
David Lindner, Rohin Shah, Pieter Abbeel +1
Since reward functions are hard to specify, recent work has focused on learning policies from human feedback. However, such approaches are impeded by the expense of acquiring such…
Combining Reward Information from Multiple Sources
Dmitrii Krasheninnikov, Rohin Shah, Herke van Hoof
Given two sources of evidence about a latent variable, one can combine the information from both by multiplying the likelihoods of each piece of evidence. However, when one or both…
Choice Set Misspecification in Reward Inference
Rachel Freedman, Rohin Shah, Anca Dragan
Specifying reward functions for robots that operate in environments without a natural reward signal can be challenging, and incorrectly specified rewards can incentivise degenerate…
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…