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One question central to Reinforcement Learning is how to learn a featurerepresentation that supports algorithm scaling and re-use of learnedinformation from different tasks. Successor Features approach this problem bylearning a feature representation that satisfies a temporal constraint. Wepresent an implementation of an approach that decouples the featurerepresentation from the reward function, making it suitable for transferringknowledge between domains. We then assess the advantages and limitations ofusing Successor Features for transfer.

Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning