Researchers demonstrate that sparse reward functions outperform dense, engineered rewards when training autonomous cyber defence agents using deep reinforcement learning. The study reveals that sparse rewards produce more reliable training, lower-risk policies, and better alignment with defender objectives without explicit penalties for costly actions.
The research addresses a fundamental challenge in applying deep reinforcement learning to cybersecurity: how reward structure shapes agent behavior and policy quality. Dense reward functions, while accelerating training by reducing exploration challenges, create perverse incentives that push agents toward suboptimal and potentially riskier defensive strategies. This matters critically because cybersecurity environments involve complex trade-offs between protection effectiveness and operational cost, where learned biases can translate to real-world vulnerabilities.
The shift toward sparse rewards in cyber defence represents a maturation of RL applications in security domains. Previous approaches prioritized training speed through heavily engineered reward functions combining multiple penalties and incentives. This study's ground truth evaluation methodology enables direct comparison across reward structures, revealing that goal-aligned sparse rewards—when sufficiently frequent—actually outperform their dense counterparts. The counterintuitive finding that sparse rewards produce policies using fewer costly defensive actions without explicit numerical penalties suggests agents develop more economical and strategically sound defensive strategies.
For cybersecurity practitioners and AI developers, this has immediate implications: reward function design deserves the same rigorous attention as model architecture. Organizations deploying RL-based defence systems should reconsider dense reward engineering in favor of simpler, goal-focused signals. The research validates that simplicity in reward design, paired with adequate signal frequency, produces more trustworthy and effective autonomous defence. As cyber threats evolve, autonomous systems that balance protection with operational efficiency become increasingly valuable. The findings indicate that future cyber gym environments and training protocols should prioritize sparse, well-aligned rewards over complex multi-component functions.
- →Sparse reward functions produce more reliable training and lower-risk cyber defence policies than dense, engineered rewards
- →Goal-aligned sparse rewards cause agents to naturally minimize costly defensive actions without explicit numerical penalties
- →Dense rewards bias agents toward suboptimal solutions that may increase rather than decrease cybersecurity risks
- →A novel ground truth evaluation method enables direct comparison of reward structures and their impact on policy quality
- →Reward function design deserves equivalent attention to model architecture when deploying reinforcement learning in security