Successor Feature Representations

Understand

Transfer in Reinforcement Learning aims to improve learning performance on target tasks using knowledge from experienced source tasks.

• Successor Representations (SR) and their extension Successor Features (SF) are prominent transfer mechanisms in domains where reward functions change between tasks.
• They reevaluate the expected return of previously learned policies in a new target task to transfer their knowledge.
• The SF framework extended SR by linearly decomposing rewards into successor features and a reward weight vector allowing their application in high-dimensional tasks.