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Interactions between Model-free and Model-based Reinforcement Learning

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Dayan, P. (2011). Interactions between Model-free and Model-based Reinforcement Learning. Talk presented at 21st Annual Conference of the Japanese Neural Network Society (JNNS 2011). Okinawa, Japan. 2011-12-15 - 2011-12-17.


Cite as: http://hdl.handle.net/21.11116/0000-0007-4A4D-1
Abstract
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 control.