Unverified50% confidenceFactExact time
Without the potential function shaping constraint, artificially set shaping rewards easily induce the policy to pursue shaping bypasses rather than truly completing the task (reward hacking).
1
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50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
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Related Claims
VerifiedClassic examples of reward hacking include a game AI spinning in place to collect rewards in a boat racing game instead of finishing the race, or a robot learning to roll rather than walk in order to maximize displacement rewards.76% similarUnverified为抓取成功设置门控条件（只有物体被稳定抓住时抬升奖励才生效）的层次化奖励设计可以有效避免未抓先抬的问题73% similarUnverifiedPotential-based reward shaping guarantees that the optimal policy remains unchanged.72% similarUnverifiedReward shaping refers to adding extra, denser guiding reward signals on top of the original sparse reward to help agents learn faster and more correctly.72% similarUnverified当奖励函数未对控制代价加以惩罚时，梯度会持续将策略推向动作边界，产生 Bang-Bang 行为，这被称为奖励作弊（Reward Hacking）72% similar
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