2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Test Where Decisions Matter: Importance-driven Testing for Deep Reinforcement Learning
Stefan Pranger, Hana Chockler, Martin Tappler +1
In many Deep Reinforcement Learning (RL) problems, decisions in a trained policy vary in significance for the expected safety and performance of the policy. Since RL policies are v…
cs.LG2022★ 2 cited
Search-Based Testing of Reinforcement Learning
Martin Tappler, Filip Cano Córdoba, Bernhard K. Aichernig +1
Evaluation of deep reinforcement learning (RL) is inherently challenging. Especially the opaqueness of learned policies and the stochastic nature of both agents and environments ma…