Researchsuccessor representationreinforcement learningnavigationcognitive maps
Agent Combines Egocentric And Allocentric Maps
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Relevance ScoreShani and Dayan publish on January 23, 2026 in PLoS Computational Biology a reinforcement-learning study that implements egocentric successor representations (SRs) composed with conventional allocentric SRs to support navigation in complex 2D environments. The paper shows that additive composition of egocentric and allocentric maps yields generalizable value functions, enabling faster learning and adaptation with fewer trials and reduced obstacle trapping. Data and code are available at the authors' GitHub repository.
