Topic: GVFs

Decomposing complex tasks into their simpler components is often the only way for animals to make any meaningful progress at all. We show that reusing the traditional reward prediction error machinery at multiple hierarchical levels allows complex tasks to be automatically decomposed in a compositional manner, leading to fast and flexible reinforcement learning. In this compositional reinforcement learning (CRL) framework, the agent computes a set of predictions for each state in the form of hierarchically organized general value functions (GVFs). Level 0 GVFs predict whether continuing straig