mlsanityData to 14 Oct 2026

Certificate-Guided Evaluation of Reinforcement Learning Generalization

arXiv 2606.00840 ↗First posted 30 May 2026cs.AIcited by 0

On the map: Deep reinforcement learning methods → Deep RL algorithms and representations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Deep RL algorithms and representations, one of 15 directions in Deep reinforcement learning methods. Builds on 14 of its 21 references that are in the map. First posted on arXiv 30 May 2026.

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14 of 21 references.

References · 14

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2016OpenAI Gymcited by 1,736
  4. 2017Hindsight Experience Replaycited by 1,136
  5. 2022Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methodscited by 151
  6. 2020Neural Lyapunov Controlcited by 142
  7. 2018Deep Online Learning via Meta-Learning: Continual Adaptation for Model-Based RLcited by 81
  8. 2021Stability Verification in Stochastic Control Systems via Neural Network Supermartingalescited by 26
  9. 2022Learning Control Policies for Stochastic Systems with Reach-avoid Guaranteescited by 23
  10. 2022Safety Certification for Stochastic Systems via Neural Barrier Functionscited by 22
  11. 2021When Is Generalizable Reinforcement Learning Tractable?cited by 20
  12. 2023Fossil 2.0: Formal Certificate Synthesis for the Verification and Control of Dynamical Modelscited by 15
2 more
  1. 2024Inductive Generalization in Reinforcement Learning from Specificationscited by 3
  2. 2020C-Learning: Horizon-Aware Cumulative Accessibility Estimationcited by 2

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