Runtime Verification of Learning Properties for Reinforcement Learning Algorithms
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Abstract (arXiv)
Reinforcement learning (RL) algorithms interact with their environment in a trial-and-error fashion. Such interactions can be expensive, inefficient, and timely when learning on a physical system rather than in a simulation. This work develops new runtime verification techniques to predict when the learning phase has not met or will not meet qualitative and timely expectations. This paper presents three verification properties concerning the quality and timeliness of learning in RL algorithms. With each property, we propose design steps for monitoring and assessing the properties during the system's operation.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 16 Nov 2023.
Builds on
6 of 19 references.
References · 6
- 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
- 2017Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithmcited by 734
- 2016Deep Reinforcement Learning for Dialogue Generationcited by 288
- 2020Neurosymbolic Reinforcement Learning with Formally Verified Explorationcited by 49
- 2020Verifiably Safe Exploration for End-to-End Reinforcement Learningcited by 34
- 2019Generalizing from a few environments in safety-critical reinforcement learningcited by 8
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Citing papers per year: 2026: 2.
2 papers.