Inductive Generalization in Reinforcement Learning from Specifications
<|reference_start|>Inductive Generalization in Reinforcement Learning from Specifications: 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 promise of our framework in generalizing to unseen policies for long-horizon tasks.<|reference_end|>
@article{subramanian2024inductive,
    title={Inductive Generalization in Reinforcement Learning from Specifications},
    author={Vignesh Subramanian, Rohit Kushwah, Subhajit Roy, Suguman Bansal},
    journal={arXiv preprint arXiv:2406.03651},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03651},
    primaryClass={cs.LG cs.AI cs.LO}
}