November 18, 2019

Cross Roads #6: Learning to Predict Without Looking Ahead

Cross Roads was proud to host David Ha of Google Brain. David gave us an overview of his paper "Learning to Predict Without Looking Ahead: World Models Without Forward Prediction".

Cross Roads #6: Learning to Predict Without Looking Ahead

According to David Ha, of Google Brian, much of model-based reinforcement learning involves learning a model of an agent’s world and training an agent to leverage this model to perform a task more efficiently. While these models are demonstrably useful for agents, every naturally occurring model of the world of which we are aware – e.g., a brain – arose as a byproduct of competing evolutionary pressures for survival, not minimization of a supervised forward-predictive loss via gradient descent. That useful models can arise out of the messy and slow optimization process of evolution suggests that forward-predictive modeling can arise as a side-effect of optimization under the right circumstances.

Read the full summary here.

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