TalkRL: The Reinforcement Learning Podcast cover image

Danijar Hafner 2

TalkRL: The Reinforcement Learning Podcast

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The Three Objective Functions of an Embodied Agent

The three categories are representation learning in the most complete sentence, exploration, and learning how to influence the environment. And they play together one benefits the other, right? But they are distinct objective functions. For example, if you don't have a diverse data set, then if you just have random actions, you can't learn a very good world model. It'll just not see interesting enough data to be valid in a white distribution of states. To get these algorithms to work and practice, you need exploration.

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