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Advantages and Limitations of using Successor Features for Transfer in Reinforcement Learning
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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. We 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.
Forward citations
Cited by 2 Pith papers
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Adaptive Policy Backbone via Shared Network
Adapting only linear layers before and after a frozen shared backbone is enough to transfer to out-of-distribution continuous-control tasks, with a theoretical argument and MuJoCo experiments.
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Successor Features for Transfer in Alternating Markov Games
A proposed transfer algorithm, GGPI, applies successor features to alternating zero-sum Markov games, but its central theorem is not proven as written.
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