The paper introduces an inductive synthesis framework that generates verifiable deterministic program approximations of neural RL policies, preserving safety invariants via counterexample-guided search over state transition systems.
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An Inductive Synthesis Framework for Verifiable Reinforcement Learning
The paper introduces an inductive synthesis framework that generates verifiable deterministic program approximations of neural RL policies, preserving safety invariants via counterexample-guided search over state transition systems.