Predictive representations can link model-based reinforcement learning to model-free mechanisms
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DOI: 10.1371/journal.pcbi.1005768
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References listed on IDEAS
- Thomas Akam & Rui Costa & Peter Dayan, 2015. "Simple Plans or Sophisticated Habits? State, Transition and Learning Interactions in the Two-Step Task," PLOS Computational Biology, Public Library of Science, vol. 11(12), pages 1-25, December.
- I. Momennejad & E. M. Russek & J. H. Cheong & M. M. Botvinick & N. D. Daw & S. J. Gershman, 2017. "The successor representation in human reinforcement learning," Nature Human Behaviour, Nature, vol. 1(9), pages 680-692, September.
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- Jaron T Colas & Wolfgang M Pauli & Tobias Larsen & J Michael Tyszka & John P O’Doherty, 2017. "Distinct prediction errors in mesostriatal circuits of the human brain mediate learning about the values of both states and actions: evidence from high-resolution fMRI," PLOS Computational Biology, Public Library of Science, vol. 13(10), pages 1-32, October.
- Lucas Lehnert & Michael L Littman & Michael J Frank, 2020. "Reward-predictive representations generalize across tasks in reinforcement learning," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-27, October.
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