A research team from Google Brain conducts a comprehensive empirical study on more than fifty choices in a generic adversarial imitation learning framework and explores their impacts on large-scale (>500k trained agents) continuous-control tasks to provide practical insights and recommendations for designing novel and effective AIL algorithms.

Here is a quick read: What Matters in Adversarial Imitation Learning? Google Brain Study Reveals Valuable Insights.

The paper What Matters for Adversarial Imitation Learning? is on arXiv.

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