Title: Using Certificates to Assess the Generalization of Reinforcement Learning

Abstract: This study introduces a logic-based framework designed to assess how well reinforcement learning (RL) algorithms generalize to tasks they have not previously encountered. The framework utilizes a specific family of inductive reach-avoid tasks, which share structural similarities in their dynamics, to facilitate the evaluation of generalization skills. Central to this approach is the introduction of a neural certificate function that acts as a rigorous test for RL generalization. This function validates the trajectories produced by RL algorithms by ensuring they meet essential conditions. Through empirical testing, we demonstrate the efficacy of this method in certifying generalization for several leading generalizable RL algorithms across difficult continuous environments. The findings indicate a strong inverse relationship between certificate function violations and performance: a lower rate of violations corresponds to a greater number of successfully completed test tasks. This underscores the framework’s utility in evaluating and differentiating the generalization strengths of various RL algorithms, offering a principled method for benchmarking this critical capability.

Source: arXiv Generated at: 2026-06-02 00:00:00 UTC