supp_material.zip (168.68 MB)

Supplementary Material for "Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data"

Version 2 2023-12-29, 19:21

Version 1 2023-08-31, 10:09

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posted on 2023-12-29, 19:21 authored by Martin Tappler, Andrea PferscherAndrea Pferscher

Supplementary Material for the submission "Learning and Repair of Deep Reinforcement Learning Policies from Fuzz-Testing Data". Please see README.md in the archive for more information.

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Keywords

Deep Reinforcement Learning (DRL)Reinforcement Learning from Demonstrations Search-Based Software Testing Policy Repair

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MIT

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