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Inductive Generalization in Reinforcement Learning from Specifications
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DOI：10.1007/978-3-032-08707-2_13.png)
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Abstract
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We present a novel inductive generalization framework for RL from logical specifications. Many interesting tasks in RL environments have a natural inductive structure. These inductive tasks have similar overarching goals but they differ inductively in low-level predicates and distributions. We present a generalization procedure that leverages this inductive relationship to learn a higher-order function, a policy generator, that generates appropriately adapted policies for instances of an inductive task in a zero-shot manner. An evaluation of the proposed approach on a set of challenging control benchmarks demonstrates the promiseofourframeworkingeneralizingtounseenpoliciesforlong- horizontasks.
Keywords:
Reinforcement Learning
Inductive Generalization
Policy Generation
Logical Specifications
Zero-Shot Learning
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