Evolving Reward-Agnostic Domain-Adapted Learning in Neuromodulated Neural Networks

Biological intelligence can learn efficiently from diverse non-reward information by exploiting assumptions about task domains, a capability that is poorly accounted for by mainstream AI learning algorithms. This study demonstrates how such Domain-Adapted Learning (DAL) can evolve from reward-driven learning through the integration of non-reward information into the learning process using neuromodulation.