Leveraging Contextual Object Embeddings and Temporal Logic to Generate Useful Auxiliary Tasks for Reinforcement Learning

Exploiting the contextual structure of objects and the compositional nature of temporal logic task specifications, this approach generates auxiliary tasks that share similar underlying exploration requirements with a given target task, enabling efficient off-policy learning.