Paper

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

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... (read more)

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