CNI Speaker Series: Sam Gershman

Sam Gershman
Computational Cognitive Neuroscience Lab
Department of Psychology and Center for Brain Science
Harvard University

Predictive foundations for reinforcement learning

In this talk, I will present a theory of reinforcement learning that falls in between "model-based" and "model-free" approaches. The key idea is to represent a "predictive map" of the environment, which can then be used to efficiently compute values. I show how such a map explains many aspects of the hippocampal representation of space, and the map's eigendecomposition reveals latent structure resembling entorhinal grid cells. I will then present evidence, using novel revaluation tasks, that humans employ such a predictive map to solve reinforcement learning tasks.A pizza lunch will be served.