Topic: sequential experience

A variety of algorithms have been proposed to account for goal-directed planning, operationalized as sensitivity to reward revaluation i.e. flexibility in the face of reward changes. These algorithms use different representations which trade off computational cost with flexibility in the face of changes in the world. In particular, model-based (MB) reinforcement learning uses tree-search through 1-step relational representations that are often thought of as ‘semantic’. In contrast, models like the successor representation (SR) store representations of sequential experience. These algorithms ca