Successor Features for Transfer in Reinforcement Learning

Abstract

Transfer in reinforcement learning refers to the notion that generalization should occur not only within a task but also across tasks. Our focus is on transfer where the reward functions vary across tasks while the environment’s dynamics remain the same. The method we propose rests on two key ideas: “successor features,” a value function representation that… (More)

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Averaging 91 citations per year over the last 2 years.

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Cite this paper

@inproceedings{Barreto2017SuccessorFF, title={Successor Features for Transfer in Reinforcement Learning}, author={Andr{\'e} Barreto and R{\'e}mi Munos and Tom Schaul and David Silver}, booktitle={NIPS}, year={2017} }