Corpus ID: 226226483

The MAGICAL Benchmark for Robust Imitation

@article{Toyer2020TheMB,
  title={The MAGICAL Benchmark for Robust Imitation},
  author={S. Toyer and Rohin Shah and Andrew Critch and Stuart J. Russell},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.00401}
}
Imitation Learning (IL) algorithms are typically evaluated in the same environment that was used to create demonstrations. This rewards precise reproduction of demonstrations in one particular environment, but provides little information about how robustly an algorithm can generalise the demonstrator's intent to substantially different deployment settings. This paper presents the MAGICAL benchmark suite, which permits systematic evaluation of generalisation by quantifying robustness to… Expand
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