Abstract: Reinforcement learning is typically conceived of in terms of how reward predictions and choice behavior adapt based on an agent's experience. However, experience is too limited to provide the brain with the knowledge necessary for adaptive behavior in the real world. To go beyond experience, the brain must harness its imaginative powers. Applications of imagination to reinforcement learning include prospective simulation for planning, and learning cached values from imaginative episodes. I will discuss how these ideas can be formalized, recent experimental evidence, and connections to other ideas being explored in CBMM.

Gershman Lab website: http://gershmanlab.webfactional.com/index.html