The Dichotomy of Model Free Versus Model Based Reinforcement Learning

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Anne Collins runs her  Computational Cognitive Neuroscience Lab at the University of California, Berkley One of the things she's been working on for years is how our working memory plays a role in learning as well, and specifically how working memory and reinforcement learning interact to affect how we learn, depending on the nature of what we're trying to learn. We discuss that interaction specifically. We also discuss more broadly how segregated and how overlapping and interacting our cognitive functions are, what that implies about our natural tendency to think in dichotomies - like MF vs MB-RL, system-1 vs system-2, etc., and we dive into plenty other subjects, like how to possibly incorporate these ideas into AI.

• Computational Cognitive Neuroscience Lab.
• Twitter: @ccnlab or @Anne_On_Tw.
• Related papers:
  • How Working Memory and Reinforcement Learning Are Intertwined: A Cognitive, Neural, and Computational Perspective. 
  • Beyond simple dichotomies in reinforcement learning.
  • The Role of Executive Function in Shaping Reinforcement Learning.
  • What do reinforcement learning models measure? Interpreting model parameters in cognition and neuroscience.

0:00 - Intro 5:25 - Dimensionality of learning 11:19 - Modularity of function and computations 16:51 - Is working memory a thing? 19:33 - Model-free model-based dichotomy 30:40 - Working memory and RL 44:43 - How working memory and RL interact 50:50 - Working memory and attention 59:37 - Computations vs. implementations 1:03:25 - Interpreting results 1:08:00 - Working memory and AI