Runtime Verification of Learning Properties for Reinforcement Learning Algorithms
Tommaso Mannucci (TNO -- Netherlands Organisation for Applied Scientific Research)Julio de Oliveira Filho (TNO -- Netherlands Organisation for Applied Scientific Research)
Tommaso Mannucci (TNO -- Netherlands Organisation for Applied Scientific Research)Julio de Oliveira Filho (TNO -- Netherlands Organisation for Applied Scientific Research)
Nov 2023
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摘要原文
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.
