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Workshop: Deep Reinforcement Learning

Invited Talk: Laura Schulz - In praise of folly: Goals, play, and human cognition

Laura Schulz


Work on autonomous agents in AI, robotics, and machine learning is often explicitly inspired by comparisons with children's play, and computational researchers and developmentalists alike tend to assume that play is rewarding because through play, agents can reduce uncertainty, increase expected information gain, and improve their predictive models of the world. I will review some developmental research consistent with this picture -- and then suggest that this account fails to capture much of what is distinctive about human play. I note that, rather than being characterized by progress towards rational goal-directed action, play typically involves manipulated utility functions, in which people willingly incur unnecessary costs to achieve arbitrary rewards. I will suggest that such "pretend" utilities may be critical not because they support better predictive models of the world but because they support ideas and plans that can change the world. That is, I propose that the reward value of play for humans is not about learning but about thinking.