Deep Successor Representation Explains Latent Learning Sensitivity
Menezes, Zeng, and Cheng (published March 24, 2026) use deep successor representation (DSR) models to explain how pre-exposure exploration shapes latent learning. They find targeted pre-exposure aligned with future reward location significantly improves subsequent reward learning compared with random, mistargeted, or no exploration, and this effect generalizes across action-selection strategies. The study links DSR-encoded spatial structure to faster goal-directed learning and provides open-source code.
Key Points
- 1Demonstrates targeted pre-exposure improves reward learning in DSR agents versus random, mistargeted, or no exploration
- 2Shows DSR encodes spatial information sensitive to behavioral statistics during exploration, shaping future goal-directed performance
- 3Imply practitioners can pretrain DSR-like embeddings with targeted exploration to accelerate downstream reward learning tasks
Scoring Rationale
Model-driven, peer-reviewed study with direct code availability; limited novelty beyond existing successor representation literature, but high applicability.
Sources
Public references used for this report.
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