Reward Shaping for Happier Autonomous Cyber Security Agents

Abstract

This work investigates the impact of the reward signal provided to deep reinforcement learning agents when training for autonomous cyber security tasks, studying reward shaping techniques that could enable agents to train more sample-efficiently and potentially converge to a better performance.