The entities, properties, events and relations named by natural languages provide a rich source of information about the kinds of abstractions humans use to interact with the world. Can we use this linguistic background knowledge to build more effective intelligent agents? This talk will explore a variety of approaches for using linguistic supervision to guide and constrain reinforcement learning: as a direct feedback signal, a source of abstract actions, a structured prior on the space of goals.


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