AI RESEARCH
Task-Induced Representational Invariances Depend on Learning Objective in Deep RL
arXiv CS.LG
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ArXi:2606.01868v1 Announce Type: new Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience. Modern deep RL has shown remarkable success across many domains, further strengthening this connection. The ability to learn abstract representations of high-dimensional state spaces underlies much of this success. However, theoretical understanding of these learned representations remains limited, hindering direct comparisons between models and animal learning.