Peter Dayan: Interactions Between Model-Free and Model-Based Reinforcement Learning

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Seminar Series from the Machine Learning Research Group at the University of Sheffield ( Talk by Peter Dayan ( of Gatsby Computational Neuroscience Unit at University College London.

Substantial recent work has explored multiple mechanisms of
decision-making in humans and other animals. Functionally and
anatomically distinct modules have been identified, and their individual
properties have been examined using intricate behavioural and neural
tools. I will discuss the background of these studies, and show fMRI
results that suggest closer and more complex interactions between the
mechanisms than originally conceived. In some circumstances, model-free
methods seize control after much less experience than would seem
normative; in others, temporal difference prediction errors, which are
epiphenomenal for the model-based system, are nevertheless present and
apparently effective. Finally, I will show that model-free and
model-based methods on occasion both cower in the face of Pavlovian
influences, and will try and reconcile this as a form of robust