This is the hardest part. A naive reward (+1 per open port) leads to scanning loops. A sparse reward (+100 only for root) leads to no learning. Effective Autopentest-DRL uses :
AutoPentest-DRL demonstrates that deep reinforcement learning can outperform static pentest automation in time-to-compromise and adaptability. While not ready for fully unattended red-team operations, it serves as a powerful augmentation for human pentesters — suggesting high-value attack paths that rigid scanners would miss. autopentest-drl