  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.
