One question central to Reinforcement Learning is how to learn a feature
representation that supports algorithm scaling and re-use of learned
information from different tasks. Successor Features approach this problem by
learning a feature representation that satisfies a temporal constraint...
present an implementation of an approach that decouples the feature
representation from the reward function, making it suitable for transferring
knowledge between domains. We then assess the advantages and limitations of
using Successor Features for transfer.