Adaptation and Evolvability through Reinforcement Learning

Abstract: The research proposal addresses the biological question of how the complexity of the genotype-to-phenotype mapimpacts adaptability and evolvability in dynamic environments, using reinforcement learning (RL) as a framework. The new mathematics being developed is a analytical and computational interrogation of how the complexity of the underlying network governing the behavior of an RL agent impacts its performance on learning tasks. Anticipated outcomes include theoretical insights into evolutionary dynamics, testable predictions about adaptability and evolvability, and experimental validation using microbial systems such as algae under temporally correlated light and temperature stresses.