90 citations · 206 across the 39 of their papers we have counts for
Showing 2018Show all
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cs.LG2018★ 22 cited
Inverse reinforcement learning for video games
Aaron Tucker, Adam Gleave, Stuart Russell
Deep reinforcement learning achieves superhuman performance in a range of video game environments, but requires that a designer manually specify a reward function. It is often easi…
cs.LG2018★ 5 cited
Active Inverse Reward Design
Sören Mindermann, Rohin Shah, Adam Gleave +1
Designers of AI agents often iterate on the reward function in a trial-and-error process until they get the desired behavior, but this only guarantees good behavior in the training…
cs.LG2018★ 14 cited
Multi-task Maximum Entropy Inverse Reinforcement Learning
Adam Gleave, Oliver Habryka
Multi-task Inverse Reinforcement Learning (IRL) is the problem of inferring multiple reward functions from expert demonstrations. Prior work, built on Bayesian IRL, is unable to sc…