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Predictive representations can link model-based reinforcement learning to model-free mechanisms

Author(s): Russek, Evan M.; Momennejad, Ida; Botvinick, Matthew M.; Gershman, Samuel J.; Daw, Nathaniel D.

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Abstract: Humans and animals are capable of evaluating actions by considering their long-run future rewards through a process described using model-based reinforcement learning (RL) algorithms. The mechanisms by which neural circuits perform the computations prescribed by model-based RL remain largely unknown; however, multiple lines of evidence suggest that neural circuits supporting model-based behavior are structurally homologous to and overlapping with those thought to carry out model-free temporal difference (TD) learning. Here, we lay out a family of approaches by which model-based computation may be built upon a core of TD learning. The foundation of this framework is the successor representation, a predictive state representation that, when combined with TD learning of value predictions, can produce a subset of the behaviors associated with model-based learning, while requiring less decision-time computation than dynamic programming. Using simulations, we delineate the precise behavioral capabilities enabled by evaluating actions using this approach, and compare them to those demonstrated by biological organisms. We then introduce two new algorithms that build upon the successor representation while progressively mitigating its limitations. Because this framework can account for the full range of observed putatively model-based behaviors while still utilizing a core TD framework, we suggest that it represents a neurally plausible family of mechanisms for model-based evaluation.
Publication Date: 25-Sep-2017
Citation: Russek, Evan M, Momennejad, Ida, Botvinick, Matthew M, Gershman, Samuel J, Daw, Nathaniel D. (2017). Predictive representations can link model-based reinforcement learning to model-free mechanisms.. PLoS computational biology, 13 (9), e1005768 - ?. doi:10.1371/journal.pcbi.1005768
DOI: doi:10.1371/journal.pcbi.1005768
ISSN: 1553-734X
EISSN: 1553-7358
Language: eng
Type of Material: Journal Article
Journal/Proceeding Title: PLoS computational biology
Version: Final published version. This is an open access article.

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