Abstract: 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 a novel revaluation task, that humans employ such a predictive map to solve reinforcement learning tasks. Finally, I will discuss the role of dopamine error signals in learning the predictive map.

Link to Meeting: https://harvard.zoom.us/j/301130600​