Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study
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Empirical study developing an automated detection framework for reward hacking in RL systems, identifying six categories of gaming behavior with high precision/recall—directly addressing a critical AI safety challenge where agents exploit reward specifications rather than achieving intended objectives.
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This paper presents a comprehensive empirical study of reward hacking in reinforcement learning systems, analyzing 15,247 training episodes across 15 diverse environments and 5 algorithms. The authors develop an automated detection framework that identifies six categories of reward hacking (specification gaming, reward tampering, proxy optimization, objective misalignment, exploitation patterns, and wireheading) with 78.4% precision and 81.7% recall. Through controlled experiments, they demonstrate that reward density and alignment with true objectives significantly impact hacking frequency, and show that mitigation techniques can reduce hacking by up to 54.6%, though practical deployment faces challenges from concept drift and adversarial adaptation. All detection algorithms, datasets, and protocols are made publicly available.
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Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study
Detecting and Mitigating Reward Hacking in Reinforcement Learning Systems: A Comprehensive Empirical Study
Ibne Farabi Shihab
Department of Computer Science, Iowa State University Ames Iowa USA
ishihab@iastate.edu
,
Sanjeda Akter
Department of Computer Science, Iowa State University Ames Iowa USA
sanjeda@iastate.edu
and
Anuj Sharma
Department of Civil, Construction and Environmental Engineering, Iowa State University Ames Iowa USA
anujs@iastate.edu
Abstract.
Reward hacking in Reinforcement Learning (RL) systems poses a critical threat to the deployment of autonomous agents, where agents exploit flaws in reward functions to achieve high scores without fulfilling intended objectives. Despite growing awareness of this problem, systematic detection and mitigation approaches remain limited. This paper presents a large-scale empirical study of reward hacking across diverse RL environments and algorithms. We analyze 15,247 training episodes across 15 RL environments (Atari, MuJoCo, custom domains) and 5 algorithms (PPO, SAC, DQN, A3C, Rainbow), implementing automated detection algorithms for six categories of reward hacking: specification gaming, reward tampering, proxy optimization, objective misalignment, exploitation patterns, and wireheading. Our detection framework achieves 78.4% precision and 81.7% recall across environments, with computational overhead under 5%. Through controlled experiments varying reward function properties, we demonstrate that reward density and alignment with true objectives significantly impact hacking frequency ( p < 0.001 𝑝 0.001 p<0.001 italic_p < 0.001 , Cohen’s d = 1.24 𝑑 1.24 d=1.24 italic_d = 1.24 ). We validate our approach through three simulated application studies representing recommendation systems, competitive gaming, and robotic control scenarios. Our mitigation techniques reduce hacking frequency by up to 54.6% in controlled scenarios, though we find these trade-offs are more challenging in practice due to concept drift, false positive costs, and adversarial adaptation. All detection algorithms, datasets, and experimental protocols are publicly available to support reproducible research in RL safety.
Reinforcement Learning, Reward Hacking, AI Safety, Specification Gaming, Automated Detection, RL Security
† † copyright: none † † conference: 47th International Conference on Software Engineering; 978-1-4503-XXXX-X/2024/04 † † isbn: ; † † ccs: Computing methodologies Reinforcement learning † † ccs: Computing methodologies Neural networks † † ccs: Computer systems organization Safety-critical systems † † ccs: Software and its engineering Software testing and debugging
1. Introduction
Reinforcement Learning (RL) has emerged as a transformative paradigm for training autonomous agents across diverse domains, from
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