Runtime Verification of Learning Properties for Reinforcement Learning
  Algorithms
Tommaso Mannucci Julio de Oliveira Filho
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.