Behavior Transformers: Cloning $k$ modes with one stone

Understand

While behavior learning has made impressive progress in recent times, it lags behind computer vision and natural language processing due to its inability to leverage large, human-generated datasets.

• Human behaviors have wide variance, multiple modes, and human demonstrations typically do not come with reward labels.
• These properties limit the applicability of current methods in Offline RL and Behavioral Cloning to learn from large, pre-collected datasets.
• In this work, we present Behavior Transformer (BeT), a new technique to model unlabeled demonstration data with multiple modes.