7 citations · 8 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 1 cited
DERAIL: Diagnostic Environments for Reward And Imitation Learning
Pedro Freire, Adam Gleave, Sam Toyer +1
The objective of many real-world tasks is complex and difficult to procedurally specify. This makes it necessary to use reward or imitation learning algorithms to infer a reward or…
cs.LG2020★ 7 cited
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…
cs.LG2018
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
Xue Bin Peng, Angjoo Kanazawa, Sam Toyer +2
Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. Effectively balancing the perf…