Unsupervised skill discovery carries the promise that an intelligent agent can learn reusable skills through autonomous, reward-free environment interaction.

When such components are expected to be embedded in safety-critical systems (e.g.,

Sequential decision-making problems typically require significant human supervision and data.

Federated learning (FL) is a distributed learning framework that leverages commonalities between distributed client datasets to train a global model.

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