Certificate-Guided Evaluation of Reinforcement Learning Generalization

2026-05-30

AI Summary

This work addresses the lack of effective and interpretable metrics for evaluating generalization in reinforcement learning (RL) to unseen tasks. The authors propose a logic-driven evaluation framework that constructs a family of structurally similar inductive reach-avoid tasks and introduces neural certificate functions to formally verify whether policy trajectories satisfy critical safety and goal-reaching specifications. By integrating formal verification into RL generalization assessment—using certificate violation rate as a quantifiable and interpretable metric—this approach offers a novel perspective on performance evaluation. Empirical results demonstrate that the violation rate exhibits a strong negative correlation with task success rates on test environments and effectively discriminates among the generalization capabilities of several state-of-the-art RL algorithms.

Abstract

This work presents a logic-driven framework to evaluate the performance of reinforcement learning (RL) algorithms in their ability to generalize to unseen tasks. Our framework defines a family of inductive reach-avoid tasks, characterized by structural similarities in task dynamics, enabling evaluation of generalization capabilities. We introduce a neural certificate function that validates trajectories generated by RL algorithms by enforcing key conditions, thereby serving as a litmus test for RL generalization. We empirically demonstrate our method's capability in certifying generalization for several state-of-the-art generalizable RL algorithms on challenging continuous environments. Our results show that a lower percentage of certificate function violations correlates with a higher number of test tasks successfully solved, highlighting the effectiveness of our framework in evaluating and distinguishing generalization capabilities of RL algorithms. This work provides a principled approach for benchmarking RL generalization.

Problem

• reinforcement learning
• generalization
• evaluation
• certificate
• benchmarking

Innovation

• neural certificate
• reinforcement learning generalization
• reach-avoid tasks
• logic-driven evaluation
• trajectory validation